A Thesis for the Degree of Master
A Study on User Profile Reasoning for Target
Advertisement in a TV Personalization Framework
Munjo Kim
School of Engineering
Information and Communications University
2006
i
A Study on User Profile Reasoning for Target
Advertisement in a TV Personalization Framework
ii
A Study on User Profile Reasoning for Target
Advertisement in a TV Personalization Framework
Advisor: Prof. Munchurl Kim
by
Munjo Kim
School of Engineering
Information and Communications University
A thesis submitted to the faculty of Information and
Communications
University
in
partial
fulfillment
of
the
requirements for the degree of Master of Science in the School of
Engineering
Daejeon, Korea
January 10, 2006
Approved by
Advisor: Prof. Munchurl Kim
iii
A Study on User Profile Reasoning for Target
Advertisement in a TV Personalization Framework
Munjo Kim
We certify that this work has passed the scholastic standards
required by the Information and Communications University as a
thesis for the degree of Master
January 10, 2006
Approved :
Chairman of the Committee
Munchurl Kim, Associate Professor
School of Engineering
Committee Member
Yongman Ro, Professor
School of Engineering
Committee Member
Changick Kim, Assistant Professor
School of Engineering
iv
M.S
Munjo Kim
20042067
A Study on User Profile Reasoning for Target
Advertisement in a TV Personalization Framework
School of Engineering, 2005, 79p
Major Advisor: Prof. Munchurl Kim
Text in English
Abstract
The traditional broadcasting services such as terrestrial, satellite and cable
broadcasting have been unidirectional mass media regardless of TV viewer’s
preferences. Recently rich media streaming has become possible via the broadband
networks.
Furthermore,
since
bidirectional
communication
is
possible,
personalcasting such as personalized TV services has been an emerging technology
by taking into account the user’s preference on content genres, viewing times, and
actors/actresses, etc. Accordingly, personal media becomes an important means for
content provision service in addition to the traditional broadcasting service as mass
media. The personal media means contents considering individual preferences. In
order to provide media services in personalized ways, TV personalization
framework and its related technologies are essential.
i
In the thesis, we propose a TV personalization framework which includes
pEPG (personalized Electronic Program Guide) generation based on user
preferences, target advertisement service, and content mobility service for
consuming contents in some personalized ways.
In the thesis, a user profile reasoning method for target advertisement is
developed in the TV personalization framework which supports the content
mobility service for multi-users.
The user profile reasoning method predicts unknown TV viewer’s gender and
ages by analyzing TV program viewing history. Based on the estimated user’s
gender and ages, targeted advertisement becomes possible. The target
advertisement is developed based on such a user profile reasoning algorithm.
In the proposed TV personalization framework, pEPG can be generated based
on the user preference values that are calculated by the user preference learning
algorithm. Users can browse, select, and consume their preferred TV program
contents from the given pEPG.
Also, the content mobility is possible based on user preference via various
kinds of user terminals in the TV personalization framework. For the content
mobility service, TV Anytime metadata is used for the content description and
consumption, and also the MPEG-21 DIA (Digital Item Adaptation) tools are
utilized in order to describe the context information for user environments, user
terminal characteristics, and user characteristics for universal access and
consumption of the contents. The content mobility is implemented to make it
ii
possible to seamlessly consume contents by a single user or multi-users among
various kinds of user terminals for the contents.
In our TV personalization framework, there are entities such as a home server,
display TV terminals, and user information terminals which enable users to receive
the target advertisement service and the content mobility service. Also, the TV
personalization framework is developed based on the TV Anytime and MPEG-21
DIA framework. To show the usefulness and effectiveness of the TV
personalization framework, we present a plenty of experimental results obtained by
using realistic TV program viewing history with 28 advertising contents and 42 TV
program contents in eight different genres from four different TV channels.
iii
Table of Contents
Abstract ...................................................................................................................... i
Table of Contents..................................................................................................... iv
List of Figures .......................................................................................................... vi
List of Tables .......................................................................................................... vii
List of Abbreviations .............................................................................................viii
1. Introduction.......................................................................................................... 1
2. TV Anytime and MPEG-21 DIA......................................................................... 5
2.1. TV Anytime Metadata Structure.................................................................. 6
2.2. TV Anytime Content Referencing ............................................................... 8
2.3. User Preference Modeling.......................................................................... 10
2.4. Content Mobility ........................................................................................ 11
3. The Proposed TV Personalization Framework .................................................. 13
3.1. Conceptual Diagram of the TV Personalization Framework ..................... 13
3.2. Content Mobility for Multi-Users .............................................................. 15
3.3. Components of the TV Personalization Framework .................................. 17
3.3.1. Home Server (HOS)......................................................................... 17
3.3.2. Display TV Terminal (DTT)............................................................ 20
3.3.3. User Information Terminal (UIT).................................................... 22
3.4. Target Advertisement................................................................................. 24
3.4.1. Introduction...................................................................................... 25
3.4.2. User Profile Reasoning Algorithm................................................... 25
3.4.2.1. Feature Extraction............................................................... 25
3.4.2.2. 1st Stage Classifier (Novel Vector Distance Measure)........ 28
3.4.2.3. 2nd Stage Classifier (Weighted-Distance k-NN) ................. 31
3.4.2.4. 3rd Stage Classifier (Normalized Majority Rule) ................ 32
3.4.3. An Advertising Content Selection Method...................................... 34
4. Experiment and Implementation Result............................................................. 39
iv
4.1. Experiment Result of User Profile Reasoning ........................................... 40
4.2. TVA Metadata of Advertising Contents .................................................... 41
4.3. TVA Metadata of TV Program Contents ................................................... 43
4.4. User Preference Metadata .......................................................................... 48
4.5. User Information Terminal Implementation Result ................................... 50
4.6. Home Server Implementation Result ......................................................... 53
4.7. Demonstration............................................................................................ 54
5. Conclusion and Future Works ........................................................................... 59
국문 요약 .............................................................................................................. 62
References............................................................................................................... 64
Appendix A............................................................................................................. 66
Acknowledgement .................................................................................................. 76
Biographical Sketch ................................................................................................ 77
Publications............................................................................................................. 78
v
List of Figures
Figure 2-1 TV Anytime Metadata Structure [7] ....................................................... 7
Figure 2-2 Tree-Structured CRID [8] ....................................................................... 9
Figure 2-3 Content Referencing using CRID [8].................................................... 10
Figure 2-4 Hierarchical Structure of User Preference DS [10]............................... 11
Figure 3-1 A Conceptual Diagram of the TV Personalization Framework ............ 15
Figure 3-2 Block Diagram of Home Server............................................................ 18
Figure 3-3 Block Diagram of Display TV Terminal............................................... 21
Figure 3-4 Block Diagram of User Information Terminal...................................... 23
Figure 3-5 An Example of the 1st Stage Classifier.................................................. 30
Figure 3-6 An Example of the 2nd Stage Classifier................................................. 32
Figure 3-7 Architecture of the Multi-stage Classifier ............................................. 33
Figure 3-8 Example of the 3rd Stage Classifier ....................................................... 34
Figure 3-9 An Example of Classification of Celebrity Endorser, Advertising Types,
and Advertising Items based on the Preference of TV viewing time.... 37
Figure 4-1 pEPG Creation based on the User Preference Metadata ....................... 49
Figure 4-2 EPG Creation based on the Keyword-based Retrieval.......................... 50
Figure 4-3 Implementation Results of User Information Terminal ........................ 51
Figure 4-4 Implementation Result of the Home Server .......................................... 54
Figure 4-5 Demonstration of the Proposed TV Personalization Framework.......... 58
vi
List of Tables
Table 3-1 Fields and Description of TV program viewing history DB .................. 26
Table 3-2 Types and Equations of Feature and the number of Features................. 27
Table 3-3 Feature Vector ........................................................................................ 28
Table 3-4 Preference Information about Celebrity Endorser from KOBACO ....... 35
Table 3-5 Preference Information about Advertising Types from KOBACO ........ 36
Table 3-6 Preference Information about Advertising Items from KOBACO......... 36
Table 3-7 Preference Table of Advertising Contents for M10s .............................. 36
Table 4-1 Experimental Result of Multi-Stage Classifier (M: Male & F: Female) 41
Table 4-2 An Example of TVA Metadata for an Advertising Content................... 43
Table 4-3 Metadata Instance of the ProgramInformationTable in TV Anytime..... 45
Table 4-4 Metadata Instance of the GroupInformationTable in TV Anytime ........ 46
Table 4-5 Metadata Instance of the ServiceInformationTable in TV Anytime ...... 46
Table 4-6 Metadata Instance of the SegmentInformationTable in TV Anytime .... 48
Table 4-7 Instance of User-Preference Metadata.................................................... 49
vii
List of Abbreviations
CRID
Content Referencing Identifier
DB
Database
DI
Digital Item
DIA
DI Adaptation
DS
Description Scheme
DTT
Display TV Terminal
DTV
Digital TV
EPG
Electronic Program Guide
GUI
Graphical User Interface
HOS
Home Server
ID
Identifier
k-NN
k Nearest Neighbor
KOBACO
Korea Broadcasting Advertising Corporation
LUT
Look Up Table
MDS
Multimedia DS
MPEG
Motion Picture Experts Group
MSC
Multi-stage Classifier
pEPG
Personalized EPG
viii
STB
Set Top Box
TAD
Target Advertisement
TCP/IP
Transmission Control Protocol/Internet Protocol
TVA
TV Anytime
UIT
User Information Terminal
XML
Extensible Markup Language
ix
1. Introduction
The current broadcasting systems such as terrestrial, satellite and cable broadcasting
have been unidirectional mass media regardless of TV viewer’s preferences. TV viewers
also are exposed to information overload because there are hundreds of channels with an
abundance of TV programs available overtime. Broadcasting and communications get
convergenced in services, terminals, and networks. Under such a broadcasting and
communications convergence environment, various kinds of user terminals exist to
consume digital multimedia contents. The various terminal types include PDA (Personal
Digital Assistant), desktop PC, Laptop, Mini-PC, PVR (Personal Video Recorder), Cell
Phone, and Smart Phone. Furthermore, since bidirectional communication is possible in
the network, personalcasting such as personalized TV service becomes an emerging
application by taking into account the user’s preference on content genres, viewing times,
and actors/actresses, etc. In the near future, it is expected that users can consume
multimedia contents anytime and anywhere through any devices via high speed wireless
network by the development of various personalized broadcasting technologies.
Accordingly, personal media considering individual preferences becomes an important
means for content provision service.
In order to provide personal media, a TV personalization framework and its
associated technologies are essential. The technologies in the TV personalization
framework include pEPG generation based on user preferences, TAD service, and
seamless contents service. Also, the TV personalization framework supports a content
1
mobility service for consuming contents anytime and anywhere via any devices in the
way TV viewers want to consume.
In order to realize a TV personalization framework, the ubiquitous multimedia
computing technologies are necessary to automatically adapt and consume multimedia
contents under various environments. That is, the technologies are not simply for creation
and consumption of contents, but for adaptation of the contents based on context
awareness of user characteristics, terminal characteristics, and network characteristics.
The content adaptation technology is one of the key technologies for the ubiquitous
multimedia computing. Also, content mobility, called as the session mobility in MPEG21 DIA, is a key technology to support seamless multimedia application. However, the
conventional content mobility is usually limited to a single user [1-3]. The other
important service in the TV personalization framework is the TAD service. The current
approaches for the TAD service are to classify the TV audience into the groups with
similar preferences by using the digital TV collaborative filtering, thus estimating
the
user’s favorite advertisement contents based on the TV program viewing history [4-6]. In
these studies, the profile information for TV viewers should be given with their genders,
jobs, and ages to their service providers. The profile information is sent to the service
servers via the network. Based on the explicit information, the service providers
customize advertisement contents to targeted users. However, such information can be
misused so that people do not want to make it publicly available. In this case, it is
difficult to provide appropriate TAD service due to lack of information about user
profiles.
2
In this thesis, we propose a TV personalization framework that makes it possible a
seamless content mobility service and a TAD service using implicit information. The
content mobility service in the proposed TV personalization framework supports both
single users and multi-users. And, for the TAD service, only implicit information of TV
program viewing history such as the viewing date, viewing time, and genres for TV
programs are required to estimate unknown TV viewer’s profile (gender and ages) with
our user profile reasoning algorithm. With the estimated user profiles, customizing target
advertisement becomes possible.
The proposed TV personalization framework utilizes TV Anytime Metadata and
Content Referencing specifications [7][8] to describe the information about the TV
program contents and advertising contents, and MPEG-21 DIA tools [9] to describe the
context information of user’s environments, terminal characteristics and user
characteristics. Based on the TVA and MPEG-21 DIA, the proposed TV personalization
framework enables TV viewers to consume their preferred contents anytime and
anywhere via any devices in a seamless way. This thesis is organized as follows: In
Chapter 2, we explain about the TVA Metadata and Content Referencing structure. And,
user preference modelling and content mobility are described with MPEG-21 DIA
structures; In Chapter 3, we show a conceptual diagram and components of the TV
personalization framework. The components include a home server (HOS), display TV
terminals (DTTs), and user information terminals (UITs). Also, a user profile reasoning
algorithm and advertising content selection method for TAD service are presented in
Chapter 3; In Chapter 4, we provide a plenty of experimental results to show the
3
performance of our user profile reasoning algorithm and present TVA metadata of
advertising contents, TVA metadata of TV program contents, user preference metadata,
and implementation and demonstration results for TV personalization framework;
Finally, the conclusion and future works are addressed in Chapter 5.
4
2. TV Anytime and MPEG-21 DIA
In this Chapter, we briefly introduce the TVA Metadata with the associated Content
Referencing structure, and MPEG-21 DIA. The TV Anytime forum is a global
association of organizations that seeks to develop specifications to enable consumers to
select and consume any contents that consumers want and anytime service based on
personal media storage such as Personal Digital Recorder (PDR) or Personal Video
Recorder (PVR) in consumer terminals [7][8]. In this thesis, we utilize the TV Anytime
specifications such as Part 3 Metadata and Part 4 Content Referencing. These two
specifications enable to create metadata about contents and to consume contents that
users select.
MPEG-21 multimedia framework aims at providing universally accessible and
uniquely consumable environment for multimedia under various conditions such as user
characteristics, network characteristics, and terminal capabilities, and natural environment
characteristics. MPEG-21 multimedia framework makes it possible to define the
interoperable transparent access to advanced multimedia content between terminals and
networks as well as the provision of network and terminal resources. The MPEG-21 DIA,
MPEG-21 Part 7, defines metadata schema to describe context information of a Digital
Item (DI), a fundamental unit for distribution and transaction in MPEG-21 framework, in
the fields of user characteristics, terminal capabilities, and network characteristics [9].
In the proposed TV personalization framework, some tools are needed for users to
receive TAD service and seamless content mobility service. TV Anytime (TVA)
5
Metadata makes it possible to describe rich information of the TV program contents and
the advertising contents. The TV program contents metadata in TVA format are used for
browsing, selecting, retrieving, and consuming the TV program contents. Also, the
advertising contents metadata in TVA format are used to retrieve a targeted advertisement
contents. The TVA Content Referencing supports location resolving of a specific content.
In MPEG-21 DIA, the session mobility tool makes it possible to seamlessly consume TV
program contents, and the context information of user characteristics, terminal
capabilities, and network characteristics helps to appropriately adapt contents under
various conditions of target terminals.
2.1.TV Anytime Metadata Structure
TVA metadata enables consumers to retrieve, to select, or to consume TV program
contents that are stored in local PDR or PDR on the networks [7]. The structure of TVA
metadata is shown in Figure 2-1. It has 4 types of metadata under the root element called
as ‘TVAMain’.
„
Content Description metadata – We can define general information about a piece of
contents. Title, genre of the content, and role of characters can be described in
ProgramInformationTable.
In GroupInformationTable,
we can
describe where a specific content belongs to. The names of the characters, defined in
ProgramInformationTable, are listed in CreditsInformatonTable.
Also, we can record opinions about the content from TV program reviewers or TV
viewers in ProgramReviewTable of the Content Description metadata.
6
„
Instance Description metadata – The information about contents providers and an
owner of contents is described in ServiceInformationTable. It is possible to
input schedule information of the contents in ProgramLocationTable.
„
Consumer metadata – The Consumer metadata is brought from User Preference
Description Scheme (DS) in MPEG-7 Multimedia Description Scheme (MDS) [10].
„
Segmentation metadata – A segment is a unit that a user enables to access, retrieve,
and consume parts of a piece of contents based on time points. In
SegmentInformationTable, the detailed information of the content such as
event, sub-title, synopsis, and background can be defined.
“TVAMain” Root Element
Content
Description
metadata
Instance
Description
metadata
Consumer
metadata
Segmentation
metadata
GroupInformationTable
ServiceInformationTable
UserPreferences
SegmentInformationTable
CreditsInformationTable
ProgramLocationTable
UsageHistory
ProgramInformationTable
ProgramReviewTable
Figure 2-1 TV Anytime Metadata Structure [7]
In the thesis, we use TVA Metadata to define two kinds of contents such as TV
program contents and advertising contents. For the description of TV program contents,
we
utilize
ProgramInformationTable,
CreditsInformationTable
in
GroupInformationTable,
Content
7
Description
metadata.
and
The
ServiceInformationTable in Instance Description metadata is used to retrieve
and
consume
contents
in
digital
home
environment.
Lastly,
we
use
SegementInformationTable in Segmentation metadata. Consumer metadata is
metadata syntax to describe how much and how a user consumes a piece of contents.
Since the consumer metadata is already defined in MPEG-21 DIA, it is not considered in
TVA metadata for the TV program contents.
For the advertising contents, we utilize ProgramInformationTable and
CreditsInformationTable
in
Content
Description
metadata
and
ServiceInformationTable in Instance Description metadata. Since the TVA
metadata of the advertising contents is used for retrieving advertising contents,
Segmentation metadata and Consumer metadata are not considered.
2.2. TV Anytime Content Referencing
In TVA metadata, Content Referencing Identifier (CRID) is used for referring to a
piece of contents [8]. CRID is a basic tool to identify a piece of contents in TVA
metadata. The syntax of CRID is ‘CRID://<authority>/<data>.’ <authority> is a body of
CRID which consists of <DNS name> <name_extension>. ‘www.kbs.co.kr;drama’ can
be an example of <authority>. The DNS name is really effective to set a unique identifier
in order to discern a piece of contents from other contents. Any strings can be used in
<data>. Some possible examples with CRIDs are given as follows.
„
CRID://www.kbs.co.kr/LarrayKingShow
„
CrId://www.kbs.co.kr/3.1.3.2_friends
8
„
crid://www.kbs.co.kr;comedy/friends
CRIDTOP
CRIDA
Locator
CRIDTOP – Friends Series
CRIDB – Season 1
CRIDB1 – Monica’s Wedding 1
CRIDB2 – Monica’s Wedding2
CRIDB
CRIDB1
CRIDB2
Locator
Locator
CRIDC
Locator
Example
Figure 2-2 Tree-Structured CRID [8]
It is also possible for CRID to refer to a physical address of contents after resolving
in CRID table. CRID has a tree structure as shown in Figure 2-2. For instance, there are
many seasons in the ‘Friends’ TV sitcom series. As shown in the blue-line box, the
CRIDTOP can be the Friend Series, and the CRIDTOP contains many seasons such as
CRIDA, CRIDB, and CRIDC. And CRIDB includes many episodes such as CRIDB1 and
CRIDB2 with the specific locators.
Figure 2-3 illustrates how to resolve location of contents. A user selects his/her
favorite content by retrieving or browsing. The CRID of the selected content can find a
location of the content through location resolving with CRID table having real location of
contents. If the selected content is one of series, server sends CRID list to the user for the
other contents in the series. Then, the user browses and selects contents from the
9
transmitted CRID list. Next, the server receives a CRID that the user selected, resolves a
real location of the content, and provides the content to the user.
Content searching,
selection, etc.
Content Consuming
Specific
content
Some kind of input
1
Content Referencing
TV-Anytime application
CRID
CRID
CRID
CRID
Locator
Locator
Locator
3
2
Content
selection
Locator
4
Location
resolving
Retrieval
Figure 2-3 Content Referencing using CRID [8]
2.3. User Preference Modeling
User preferences are valuable information for user-oriented or personalized
broadcasting services. Also, there are a plenty of algorithms and methods to define the
user preferences. One simple solution is to let users set the preference values for given
preference types. In this case, the users have to be aware of the preference types and the
semantics of specific values. It is more appropriate to compute the user preferences based
on the TV program viewing history of contents. By analyzing the TV program viewing
history data, the user preference values can be automatically and adaptively obtained
from user preference learning algorithms under changes in time, dates, and a day of week.
The users can easily access and consume their preferred contents by the automatically
10
computed preference values from the user preference learning algorithms. Figure 2-4
shows a hierarchical structure of a User Preference Description Scheme (DS) [10].
Figure 2-4 Hierarchical Structure of User Preference DS [10]
In Figure 2-4, the User Preference DS contains the Browsing Preference DS,
Filtering and Searching Preference DS, and User Identifier. The User Identifier is used to
identify users for user preference description data. The Filtering and Searching Preference
DS specifies creation preference, source preference, and classification preference. The
Browsing Preference DS has the Summary Preference DS which contains the
SummaryTypePreference, PreferredSummaryTheme, and other summary types which are
very useful in personalized contents consumptions.
2.4. Content Mobility
Content mobility is a concept of seamless content consumptions to other devices
from an initial device. The content mobility is defined as session mobility in MPEG-21
DIA [9]. From the view of communications, the session mobility can be regarded as a
11
service which the initially established session consecutively maintains in the other
services. However, the session mobility can be thought of as content mobility from the
view of contents because of seamless contents consumptions. So, we rename the
terminology session mobility into content mobility and extend its concept towards multiusers in this thesis. The MPEG-21 DIA tool, a supporting type of content mobility,
enables to capture the configuration state of the content DI, which is defined by the state
of selection elements, and application stage information, which pertains to specific
information of the application currently rendering the DI. By using the content mobility
tool in the proposed TV personalization framework, one or more users are able to
seamlessly consume a piece of contents without any space or time limitation. The content
mobility tool saves and gives detail explanation about the interactions that was
established between a user and DI which the user was consuming.
12
3. The Proposed TV Personalization Framework
In this Chapter, we explain about a conceptual diagram and components of the TV
personalization framework. The components are a home server (HOS), display TV
terminals (DTTs), and user information terminals (UITs). Also, user profile reasoning
algorithm and advertising content selection method for TAD service are described.
3.1. Conceptual Diagram of the TV Personalization Framework
Figure 3-1 shows the conceptual diagram of the proposed TV personalization
framework. The proposed framework works with TVA metadata and MPEG-21 DIA
metadata. There are two types of TVA metadata such as TVA metadata for TV program
contents and TVA metadata for advertising contents. The TVA metadata for TV program
contents is used to describe a piece of TV program contents in details and to create pEPG
through a user preference learning algorithm, and the other TVA metadata is utilized to
retrieve advertising contents based on inferred user profile (gender and ages). Also, the
MPEG-21 DIA deals with context information of terminal devices. The presented TV
personalization framework consists of three entities such as a HOS, DTTs, and UITs.
The HOS has high computing power and includes as follows.
„
Storage – Store TV program and advertising contents.
„
DB – Maintain TVA metadata DB for TV program and advertising contents.
„
Transcoding Engine –Transcode adaptively the original contents for target terminals.
„
Streamer – Deliver TV program and advertising contents to target terminals.
13
„
User Preference Module – Store TV program viewing history data, analyze the data,
and create user preference values through user preference learning algorithms, and
periodically update user preference values.
„
TAD Module – Predict unknown user’s profile (gender and ages) and select an
advertising content based on the advertising content selection method.
The DTTs are equipped with IP-STB and contain as follows.
„
Displayer – Display contents delivered from the HOS.
„
Network – Communicate with the HOS via wired network.
„
MPEG-21 DIA Handler – Send the context information about the DTT
characteristics to the HOS.
„
Content Handler – Perform the content mobility and display contents in a segment
unit for only TV program contents since the SegmentInformationTable is not used
for the advertising contents.
The UITs are implemented on mobile devices such as Smart Phone and PDA. These UITs
include following functions.
„
Network – Communicate with the HOS via wireless network.
„
Metadata Parser – Parse pEPG or retrieval result in the form of TVA metadata from
the HOS.
„
Displayer – Display the contents delivered from the HOS.
„
MPEG-21 DIA Handler – Send the context information about the UIT characteristics
to the HOS.
14
„
GUI – Support easy and convenient usability to browse, select, and consume
contents.
„
Retrieval – Retrieve only TV program contents through keyword-based retrieval and
segment-based retrieval.
„
Content Handler – Handle the content mobility for seamless consumption of the TV
program contents for multi-users. However, content mobility for advertising contents
is not available.
Transcoding Engine Target Advertisement
for Content
Module
Content
Interceptor
I am consuming
my content in the
living room
Content
Transcoding
Content selection
Content Mobility Request
MPEG-21 DIA metadata
Content selection
Content Mobility Request
MPEG-21 DIA metadata
Network
Content & Adapted
content streaming
pEPG
TV-Anytime Metadata
User Profile
Select
Advertising
contents
Home
Server
(HOS)
Content / Metadata
Retrieval
pEPG Generation
Let me consume
your content in the
kitchen.
User Information
Terminals
(UITs)
Display TV Terminals(DTTs)
TV Program
+
Advertising
Contents Storage
TV Program
+
Advertising
TVA Metadata DB
Figure 3-1 A Conceptual Diagram of the TV Personalization Framework
3.2. Content Mobility for Multi-Users
Content mobility is a concept of seamless content consumptions to other devices
from an initial device. Also, the content mobility in TV personalization framework is
only suitable for TV program contents. The content mobility for advertising contents is
not available in our TV personalization framework.
15
The traditional content mobility service supports the content mobility only for a
single user. Thus, the conventional systems are useful and convenient for single users.
For example, a family watches a TV program content at home under the digital home
environments, and some family members may want to consume the content on their own
terminal devices at different times. In this case, the existing content mobility service can
not support the content mobility for multi-users.
In this thesis, we suggest a content mobility service which enables multi-users to
seamlessly consume a piece of contents. The proposed content mobility service is the
basis of the TV personalization framework. The new seamless content mobility service in
the TV personalization framework has a content interceptor for content mobility of multiusers. For instance, family members watch TV program content together on a specific
DTT at the same time, and when one of them wants to consume the content on another
DTT, the content interceptor makes it possible to intercept the context information of the
currently playing TV content and to send the context information to the HOS without
possession of the DTT on which TV program content is rendering. When the one requests
consumption of the content on a different DTT at the different time, the HOS utilizes just
received context information and streams the requested contents of the user.
In order for the HOS to perform the function of content interceptor, the HOS not
only manages context information for each user, but also monitors the possession of
DTTs by a user. When a user selects a DTT which is already possessed by another user,
the user who requests consumption has (a) to give up the consumption of a piece of
16
contents, (b) to consume the content on another DTT, or (c) to invoke content interceptor
function.
3.3. Components of the TV Personalization Framework
In this section, we describe the functionalities and block diagrams of the major
entities in the TV personalization framework. The major entities in the TV
personalization framework are a HOS, DTTs, and UITs. Those components are illustrated
as block diagrams.
3.3.1. Home Server (HOS)
In the proposed TV personalization framework, the HOS consists of a storage for TV
program and advertising contents, a DB to handle TVA metadata for TV program and
advertising contents, a transcoding module to adaptively transcode contents based on
terminal characteristics, and a content streaming module. Also, it has a user preference
module and a TAD module. Figure 3-2 shows a block diagram of the HOS. The HOS
interprets network messages through the HOS message commander. The network
messages have 6 types of messages such as pEPG Request, Retrieval Request,
Generic
Command
Request, Content
Request, TAD
Request, and
Session Save request.
„
pEPG Request – When pEPG request message is received from a UIT, the HOS
automatically computes genre preferences of TV program contents based on the user
preference type in MPEG-21 DIA by the user preference learning algorithm.
According to the preference values, pEPG metadata is created by the TVA metadata
17
generator. The TVA metadata generator crates pEPG metadata obtained from the
TVA metadata DB. The created pEPG metadata in the form of TVA metadata is
transmitted to UIT through the network message sender.
Network
Message
Sender
Context Info
Manager
Content
Manager
Content
Sender
Content
Streamer
Advertising Content
Selection Method
Session Info
Manager
Context
Info
Request
TAD
Request
Session
Save
HOS
Message
Commander
Request
pEPG
Request
Content
Request
Generic
Command
Request
Retrieval
User Profile
Reasoning Algorithm
User Preference
Learning Algorithm
Contents
Retrieval Manager
Content
Storage
Generic
Command
Manager
Content
Transcoder
Network
Message
Sender
TV Program
+
Advertising
TVA Metadata
DB
TVA
Metadata
Generator
Figure 3-2 Block Diagram of Home Server
„
Retrieval Request – A user can request content retrieval via GUI of UIT.
Content retrieval manager makes a query based on the requested keywords from the
user. The retrieval manager searches TVA metadata from the TVA metadata DB.
The TVA metadata generator creates TVA metadata based on the retrieval result in
the same way to make pEPG metadata. The generated TVA metadata can be sent via
the network message sender.
„
Content Request – At the time of content consumption request from a user, the
context information manager understands context information of a target terminal
and sends the result to the content manager. The content manager gives the result to
18
the content streamer. Then, the content streamer acquires the requested content from
the content storage. If the content transcoding is needed based on the result from the
context information manager, the content is transmitted to the content transcoder. If
not, it is directly sent to the content sender. The content sender streams contents via
the network message sender.
„
TAD Request – When a user logs into the HOS with a UIT for the first time, the
HOS not only generates pEPG metadata, but also makes it possible to deliver
advertising contents to the user. At the time the user logs into the HOS, since the
HOS knows the user ID, the HOS extracts feature vectors from the user’s TV
program viewing history. The extracted feature vectors are input to the user profile
reasoning algorithm. The user profile reasoning algorithm infers the user’s gender
and ages. Based on the estimated gender and ages, the advertising content selection
method obtains an advertising content from the TVA metadata DB. The obtained
advertising content is delivered to the user in the same way of the content request.
„
Session Save Request – In the HOS, the session information manager stores
all the session information of family members. The session save request only
supports TV program contents. There are two types of the session save requests.
First, when a user stops consuming a piece of contents, the session information
manager automatically stores the session information by using the user ID, a
consumption terminal, the amount of consumption, and content ID. Second, since the
HOS knows where a specific content is being rendered, it is possible to utilize the
function of content interceptor. That is, when the HOS receives the content
19
interceptor command from the UIT, the HOS can receive the session information
from a DTT currently rendering a piece of contents because the HOS knows the title
of the content and the terminal on which the content is consumed. The received
session information from the DTT is sent to the UIT which has requested the content
interceptor.
„
Generic Command Request – The HOS also manages DTTs’ states, remote
control commands delivery from a UIT to a DTT, network states, etc. When these
generic command requests are received, the generic command manager takes care of
the requested commands and gives the result of requested command to the UIT via
the network message sender.
3.3.2. Display TV Terminal (DTT)
The DTT interprets network messages from the HOS through the wired network. The
DTT sends the context information of DTT to the HOS or playbacks a piece of contents.
The DTT is also possible to perform the content mobility or to render the segments of the
content. Since the DTT has higher computing power than UITs, it can render high quality
contents. The block diagram of DTT is shown in Figure 3-3. The DTT understands
messages through the DTT message commander in Figure 3-3. There are 3 types of
messages such as Remote Control Command, Content Packets, and
Context Information Request.
20
DTT
Message
Commander
Content Packet
Receiver
Content
Packets
Remote
Control
Command
Network
Message
Sender
Content
Decoder
Context
Info
Request
Content
Rendering
Interpreter of
Remote Control
Command
Realization of
Remote Control
Command
Acquirer of
Context Info
Acquirer of
Player Message
Display TV Terminal
Player
Figure 3-3 Block Diagram of Display TV Terminal
„
Remote Control Command – The remote control command comes into the
DTT through the DTT message commander by the request of a UIT. The interpreter
of remote control command understands what type of remote control commands has
been given, and it sends the result to the realization of remote control command. The
realization of remote control command performs the remote control command, and
shows the result through the DTT player. Also, the message of DTT player is
acquired by the acquirer of player message and is sent to the HOS.
„
Request Context Information – When the DTT receives the context
information request, it is deployed into the DTT player. The acquirer of context
information obtains the context information of DTT and transmits the information
through the network message sender.
21
„
Contents Packets – When a user requests consumption of a piece of contents
through the UIT, the requested content is sent to the DTT via the TCP/IP. The
transmitted content packets through the TCP/IP are sent to the content packet
receiver. The content packet receiver sends the packets to the content decoder. Then,
the decoding result of the content packets is given to the content rendering. Finally,
the user is able to consume the content on LCD or PDP by the GUI of the DTT.
3.3.3. User Information Terminal (UIT)
The most important entity in the TV personalization framework is the UIT. The UIT
can be a PDA, Smart Phone, Cell Phone, or Mini-PC and is able to communicate with the
HOS via the wireless network. Also, it receives pEPG metadata in the TVA Metadata
format from the HOS and pares it. Based on the result of parsing pEPG metadata, the UIT
enables a user to browse, select, or consume a piece of contents. In addition, the UIT
provides keyword-based retrieval for contents retrieval functionality and can retrieve
segments of the content. Figure 3-4 shows the block diagram of the UIT. The UIT
interprets what messages has been requested through the UIT message commander. There
are 4 types of network messages such as TVA XML, Generic Command, Content
Packets, and User Request Command of GUI.
„
TVA XML – When a user logs into the HOS, the user requests pEPG. The user can
request content retrieval as well. After the HOS generates pEPG or retrieval result,
the generated TVA metadata is sent to the UIT. When the network message is TVA
22
XML, the UIT message commander sends the generated TVA metadata to the TVA
metadata parser. The parsing result is shown by the pEPG browser in the GUI.
„
Generic Command – A user can request the user request command via the GUI of
UIT. The result of the requested command is transmitted to the UIT from the HOS.
The request commands can be the content mobility request or the network states. The
results are displayed on the GUI of the UIT.
UIT
Message
Commander
TVA
XML
Content
Packets
Content
Decoder
Generic
Command
TVA Metadata
Parser
Acquirer of
Context Info
Session Info
Command
GUI
Command
Content
Rendering
Graphical
User Interface
pEPG
Browser
Network
Message
Sender
Content Packet
Receiver
pEPG
Player
User Request
Command
pEPG
Remocon
Figure 3-4 Block Diagram of User Information Terminal
„
Content Packets – By requesting a piece of contents consumption from the
UIT, the content packets are sent to the UIT through the wireless network and the
transcoder in the HOS. The transmitted content packets are given to the content
packet receiver, and the packet receiver sends the packets to the content decoder. The
content decoder makes it possible to decode the content packets, and the decoding
23
results are transferred to the content rendering. The content consumption is done by
the pEPG player in the GUI.
„
User Request Command – A user can request commands via the GUI of the
UIT. The user request command is divided into 3 types of requests. When the user
request command is a transmission of the context information, the acquirer of context
information obtains the UIT’s environments and network states and sends the context
information to the HOS through the network message sender. The session
information request is the command of the content interceptor. And, the last request
is the GUI commands which are requested by the pEPG browser and pEPG remocon
in the GUI. When the GUI commands are requested, the commands such as remote
control request, content consumption request, content retrieval request or content
mobility request are transmitted to the HOS through the network message sender.
3.4. Target Advertisement
In this section, we explain about the TAD service which is one of the major parts in
this thesis. The TAD service is based on the user profile reasoning algorithm and
advertising content selection method. The user profile reasoning algorithm predicts a
user’s profile (gender and ages). Based on the inferred profile, the advertising content
selection method selects an advertising content by utilizing preferences and TVA
metadata of advertising contents. For the TAD service, we present a multi-stage classifier
(MSC) as the user profile reasoning algorithm and the advertising content selection
method in detail.
24
3.4.1. Introduction
The current unidirectional advertisement broadcasting service provides advertising
contents depending on the popularity of TV programs, viewing rate, age group of TV
viewers, and time bands with TV programs, but it is not efficient to provide the useful
information to the user. Therefore, the TAD service is expected to be one of the important
services in the personalized broadcasting environments. In the current TAD service, TV
viewers are requested to give their preference information about advertising items and
types to advertising content providers. However, private information can not be publicly
available in general. So, it is difficult to provide appropriate TAD service to specific
users. By utilizing implicit information of TV program viewing history, we introduce a
user profile reasoning algorithm and advertising content selection method for the TAD
service.
3.4.2. User Profile Reasoning Algorithm
The most important method for the TAD service is the user profile reasoning
algorithm. We explain about the MSC for user profile reasoning algorithm, and how to
extract feature vectors for the proposed algorithm.
3.4.2.1. Feature Extraction
The feature values for the user profile reasoning algorithm can be obtained from the
TV program viewing history. In this thesis, we use EPG data which consists of titles,
date, genres, and channels of TV program contents. The TV program viewing history has
various fields as shown in Table 3-1.
25
Field Name
id
profile
date
Description
TV viewer’s ID
TV viewer’s gender and age group
program broadcasting date
dayofweek
a day of the week for program
subscstart_t
beginning time point of watching TV
subscend_t
ending time point of watching TV
programstart_t scheduled beginning time of program
programend_t
title
channel
genre
scheduled ending time of program
title of program
channel of program(6 channels)
genre of program(8 genres)
Table 3-1 Fields and Description of TV program viewing history DB
Feature values have to meet the following conditions to infer a TV viewer’s profile
(gender and ages) which is used in the TAD module of the TV personalization framework
as shown in Figure 3-1.
•
( subscend _ t − subscstart _ t )
≥ TTh
( programend _ t − programsta rt _ t )
•
∑ PGs ≥ C
Th
( subscend _ t − subscstart _ t )
( programend _ t − programsta rt _ t )
is a value of the consumed time
proportional to scheduled duration of a TV program, and TTh is a threshold value for the
consumed duration of a TV program.
∑ PGs
is the number of consumed TV programs
for a specific duration. The consumed TV programs have to be greater than TTh . And
26
CTh is a threshold value for the number of consumed TV programs. The percentage of
the watching TV programs has to be greater than TTh , and the number of consumed TV
programs have to be greater than CTh . With the satisfaction of two conditions, the
number of feature values, types of the feature values, and their equations are described in
Table 3-2.
Types and Equations of Feature Values
z
Genre Probability based on the number of counts(GPRC)
z
GPRCi ,k ,a = GCi,k ,a / ∑ GCi,k ,a
I
#
8
i =1
z
Genre probability based on the amount of consumption time(GPRT)
z
GPRTi ,k ,a = GTi,k ,a / ∑ GTi,k ,a
I
8
i =1
z
z
Average viewing time(AVT)
1
AVTk ,a = VTk ,a / TotTime
z
Channel probability based on the amount of consumption time(CPR)
z
CPR j ,k ,a = CT j,k ,a / ∑ CT j,k ,a
J
6
j =1
Table 3-2 Types and Equations of Feature and the number of Features
In Table 3-2, GCi,k ,a is the frequency of watching genre i of a TV viewer k in
gender and age group a during a pre-determined period, and GTi,k ,a is the
consumption time of genre i of the TV viewer k in the group a during the period.
Also, VTk ,a is the consumption time of the TV viewer k in the group a during the
27
period, and TotTime is the total time of the period. Lastly, CT j ,k ,a is the consumption
time of channel j of the TV viewer k in the group a during the period. I and J
are the total number of the genres and channels.
By utilizing feature values and equations in Table 3-2, we can make each TV viewer’s
feature vector in a specific day of a week and duration. The feature vector can be
expressed as Table 3-3.
Index
1~8
9 ~ 16
17
Feature Values GPRC GPRT AVT
18 ~ 23
CPR
Table 3-3 Feature Vector
The feature vector in Table 3-3 has 23 feature values. There are 2 types of feature
vectors such as the feature vector for each TV viewer and a group feature vector to
represent a group of TV viewers based on the gender and ages. The TAD module in
Figure 3-1 has LUT containing TV viewers’ feature vectors which are grouped by gender
and ages. Also, the groups of TV viewers based on the gender and ages are divided by 10
years increment. The MSC infers the TV viewer’s profile (gender and ages) from the
feature vectors in LUT. In this thesis, we extract the feature vectors from Monday to
Friday because most gender and age groups have similar viewing patterns in the
weekend.
3.4.2.2. 1st Stage Classifier (Novel Vector Distance Measure)
The 1st stage classifier is performed by a metric to measure between two feature
vectors. The similarity measure between two vectors is calculated by the vector
28
correlation and the normalized Euclidean distance. The vector correlation to measure the
similarity between two vectors is obtained from Equation (1) [11].
m
VC(x, y ) = cosθ =
x⋅y
=
x⋅y
∑x y
i =1
i
m
i
m
∑x ∑y
i =1
2
i
i =1
(1)
2
i
However, the vector correlation only measures the angle between two vectors. The vector
correlation does not consider the distance between vectors.
The normalized Euclidean distance uses the variances as the normalized term of the
Euclidean distance. The variances are obtained from feature values in feature vectors for
a specific group of gender and ages. The Equation (2) shows the normalized Euclidean
distance.
ED(x, y ) =
m
∑
( xi − yi ) 2
i =1
σ i2,g
(2)
In Equation (2), g indicates a specific group of gender and ages. The normalized
Euclidean distance only calculates the distance between two vectors. So, we propose a
novel method to measure the distance between two vectors. The proposed method
considers the distance and the direction of vectors at the same time. The proposed
distance measure between two vectors gives weights to the vector correlation and the
normalized Euclidean distance. The novel vector distance metric between two vectors,
v i and v t , is shown in Equation (3). In Equation (3), I is the index of a specific
group. Also, wI ,V = VC(G I , v t ) and wI , E = ED(G I , v t ) . G I is a group feature
29
vector of the group I . That is, wI ,V and wI ,E are the coefficients of the vector
correlation and the normalized Euclidean distance between the group feature vector G I
and v t . In addition, v k is the k th feature vector in the LUT, and v t is the TV
viewer’s feature vector to infer profile.
Dist( v k , v t ) = GVC( v k , v t ) + GED( v k , v t )
GVC( v k , v t ) = (1 - wI ,V ) × (1 − VC( v k , v t ))
(3)
GED( v k , v t ) = wI , E × ED( v k , v t )
The 1st stage classifier measures the vector distance with Equation (3) as the novel
vector distance metric and builds the vector distance table (VDT).
Test ID#1 : Comic( 0.35 ), Children( 0.2 ), …….
VDT
Look Up Table (LUT)
ID
Feature Vector
1(G1)
Comic( 0.35 ), Children( 0.2 ), …….
2(G2)
Comic( 0.25 ), Children( 0.1 ), …….
•
•
•
•
•
•
N (G14)
After sorting
in ascending order
The novel distance metric
measures the distance
between a vector in LUT
and Test ID#1 vector.
Comic( 0.1 ), Children( 0.05 ), …….
ID
Distance
1(G1)
0.001
2(G2)
0.015
•
•
•
•
•
•
N (G14)
0.53
Figure 3-5 An Example of the 1st Stage Classifier
Figure 3-5 illustrates an example of the 1st stage classifier. In Figure 3-5, when a
feature vector for inferring profile is sent to the TAD module, the vector distance can be
obtained between each feature vector in the LUT and the feature vector for inferring
profile. Then, the VDT can be created with the obtained distance values after sorting in
ascending order as the 1st stage classifier. The 1st stage classifier simply sorts the
30
distances between a feature vector under test and all the reference vectors in the LUT.
The sorting indexes for the distances are input to the 2nd stage classifier.
3.4.2.3. 2nd Stage Classifier (Weighted-Distance k-NN)
The 2nd stage classifier is calculated with the VDT of the 1st stage classifier. We
utilize k -NN method in the 2nd stage classifier. However, the normal k -NN method
makes a decision depending on the number of k s in the higher rank. Since the k -NN
method only considers the number of k s in the higher rank, the method does not utilize
the distance values of k s to classify. So, the 2nd stage classifier in this thesis is
performed with the weighted-distance k -NN (WD k -NN) considering the distance
values of k s in the higher rank [12].
The WD k -NN utilizes k s in the higher rank of VDT in Figure 3-5, and the
equation for WD k -NN of a specific group I is shown in Equation (4).
WDK ( I ) =
∑1 / VDT(i)
i∈I ,i∈k
N k
∑∑1 / VDT( j, G I )
(4)
I =1 j =1
In Equation (4), i ∈ I , I is the index of a group, and k is k value in k -NN.
VDT(i ) is a vector distance value belonging to the number of k s of k -NN and the
group I . N is the total number of groups, and VDT( j , G I ) is vector distance values
of G I group in the number of k s of k -NN. Through Equation (4), we can make the
weighted-distance k -NN table (WDKT) for groups of k . Figure 3-6 illustrates an
31
example of the 2nd stage classifier when k is 7. In Figure 3-6, the yellow box shows
how to obtain the WDKT from the VDT and Equation (4). After the 2nd stage classifier,
we can obtain a TV viewer’s profile inference result and the maximum of the WD k -NN
values in the WDKT for each day of the weekday. The 2nd stage classifier takes the k
reference vectors which have the k smallest distances to each group with gender and
ages. The 2nd stage classifier then computes the weighted distances between the feature
vector under test and the m(≤ k ) groups with their gender and ages. Therefore, the
output of the 2nd stage classifier is the maximum similarity values for their corresponding
groups for all dates.
VDT
Index
Distance
1(G1)
0.051
WDKT
Total Sum of (1/Distance) = 55.2006
2(G2)
0.115
3(G1)
0.125
4(G2)
0.135
5(G2)
0.145
WDK(G3)=(1/0.355)/55.2006
6(G3)
0.355
WDK(G4)=(1/0.563)/55.2006
7(G4)
0.563
WDK(G1)=(1/0.051+1/0.125)/55.2006
WDK(G2)=(1/0.115+1/0.135+1/0.145)/55.2006
Index
WD k-NN
1(G1)
0.5004
2(G2)
0.4167
3(G3)
0.051
4(G4)
0.0322
Figure 3-6 An Example of the 2nd Stage Classifier
3.4.2.4. 3rd Stage Classifier (Normalized Majority Rule)
The WDKTs for the weekday can be made after the 1st and 2nd stage classifiers. The
3rd stage classifier calculates with majority rule table (MRT) utilizing the maximum
values
in the WDKTs and the inference results for the weekday. The 3rd stage classifier
estimates the final inference with the normalized majority rule.
32
삭제됨: discrimination; that is,
t
서식 있음: 영어(영국), 글자
위치 내림: 5 pt
Figure 3-7 shows the architecture of the MSC. The testing data in Figure 3-7 is a TV
viewer’s data for inferring profile, and the feature vectors of the testing data for the
weekday are extracted from the user’s TV program viewing history. And, the TAD
module has reference feature vectors of many TV viewers who are grouped by gender
and ages. The reference feature vectors are the training data in Figure 3-7. After the 1st
and 2nd stage classifiers, the WDKTs have the WD k -NN values and the inference
results for the weekday. The final inference result is driven from the normalized majority
rule after drawing up the MRT by using the maximum values in the WDKTs and
inference results for the weekday.
Mon
Testing Data
Mon
Feat Vector
Extraction Training Data
Look Up
Table
Novel Vector Distance
Vector Dist
Table
WD k-NN
Metric
WD k-NN
Table
Tue
Testing Data
Tue
Feat Vector
Extraction Training Data
Look Up
Table
Novel Vector Distance
Vector Dist
Table
WD k-NN
Metric
WD k-NN
Table
Novel Vector Distance
Vector Dist
Table
WD k-NN
Metric
WD k-NN
Table
Thurs
Testing Data
Thurs
Feat Vector
Extraction Training Data
Look Up
Table
Novel Vector Distance
Vector Dist
Table
WD k-NN
Metric
WD k-NN
Table
Fri
Testing Data
Fri
Feat Vector
Extraction Training Data
Look Up
Table
Novel Vector Distance
Vector Dist
Table
WD k-NN
Metric
WD k-NN
Table
Wed
Feat Vector
Extraction Training Data
Wed
Testing Data
Feat Vector
Extraction
Feature
Vector
Extraction
Look Up
Table
User Profile Reasoning
Algorithm
st
1 Stage
Classifier
nd
2 Stage
Classifier
The 3rd
Stage Classifier
Majority Rule Table
Using Maximum in the
WD k-NN Table
Normalized
Majority Rule
Profile
Inference
Result
Figure 3-7 Architecture of the Multi-stage Classifier
The normalized majority rule is shown in Equation (5). In Equation (5), I is the
index of the inference result for the weekday, and D means the weekday from Monday
to Friday, and WDKT(d ) is a value of WDKT in d day of the week. In the 3rd stage
33
classifier, the profile inference result of a TV viewer can be obtained from the MRT and
Equation (5).
NMR ( I ) =
max{WDKT ( d ) | d ∈ D}
D
∑ max{WDKT (d ) | d ∈ D}
(5)
d =1
Figure 3-8 illustrates an example of the 3rd stage classifier. The MRT in Figure 3-8
has the maximum values in the WDKTs and the inference result of the 2nd stage classifier.
삭제됨: role of the
The yellow box in Figure 3-8 shows how to calculate Equation (5) with the MRT. The 3rd
stage classifier computes the weighted similarity of the all dates for each group with
gender and ages. Based on this similarity, the best possibility group with gender and ages
is determined as the output by the 3rd stage classifier.
Majority Rule Table
(MRT)
Index
Max of
WD k-NN
Inference
Result
Mon
0.4772
M10s
Tue
0.4687
M10s
Wed
0.4593
M10s
Thurs
0.732
M0s
Fri
0.682
M0s
Sum of Max WD k-NN = 2.8192
NMR(M10s) = (0.4772 + 0.4687 + 0.4593)/ 2.8192
NMR(M0s) = (0.732 + 0.682)/2.8192
M10s – 0.4984
M0s – 0.5016
Inference Result
“Male, 0 ~9”
Figure 3-8 Example of the 3rd Stage Classifier
3.4.3. An Advertising Content Selection Method
In this section, we explain how to select an advertising content based on the user
profile inference. The advertising content is selected from the advertising content
34
selection method which utilizes TVA metadata and preference values of advertising
contents from the Korea Broadcasting Advertising Corporation (KOBACO).
After creating TVA metadata of advertising contents, we need to select an
advertising content based on the profile (gender and ages) inference result. However, in
order to select advertising contents, it is necessary to know preference information about
advertising contents. Also, the preference information has to be the confidential
information. In this thesis, we utilize a survey result from the KOBACO in order to know
gender and age groups’ preference information in the fields of celebrity endorser,
advertising types, and advertising items [13]. The survey result of the preference
information is shown in Table 3-4, 3-5, and 3-6. In Table 3-4, preference of celebrity
endorser is presented by the percentage. The preference values for advertising types and
advertising items in Table 3-5 and Table 3-6 are obtained from the pre-classified lists, and
the values are up to 6.
M10s
M20s
M30s
M40s
Jeon, JH
Jeon, JH
Lee, HL
Lee, HL
1
22.4
24.4
12.5
11.3
Kwon, SW Lee, HL
Lee, YA
Lee, YA
2
12.4
11.2
12.2
8.9
Lee, HL
Lee, YA
Jeon, JH
Jeon, JH
3
8.0
6.6
11.4
7.7
Kim C
Song, HK Song, HK Song, HK
4
4.4
4.6
5.2
4.0
Lee, YA Kwon, SW Ahn, SK
Ahn, SK
5
3.6
3.4
3.4
3.3
Song, HK
Kim C
Kwon, SW Kwon, SW
6
3.6
2.5
2.9
2.9
Rain
Kim, JE
Han, SK
Kim, JE
7
2.6
2.1
2.6
2.3
Han, YS
Jung, WS
Kim, JE
Kim, NJ
8
2.3
2.1
2.5
2.0
Lee, NY
Han, YS
Kim, NJ
Jeon, IH
9
2.1
1.8
2.2
1.9
Boa
Lee, NY
Song, YA Choi, MS
10
2.1
1.6
1.7
1.9
Over M50s
Lee, YA
9.5
Lee, HL
8.7
Ahn, SK
3.2
Kim, HJ
3.0
Choi, BA
2.8
Kim, JE
2.5
Ko, DS
2.3
Jeon, JH
1.7
Chae, SL
1.7
Song, HK
1.5
F10s
F20s
F30s
F40s
Over F50s
Jeon, JH
Jeon, JH Kwon, SW Lee, YA
Lee, YA
16.8
15.1
11.7
11.2
9.7
Kwon, SW Kwon, SW Lee, YA Kwon, SW Kwon, SW
12.0
13.8
11.6
7.7
7.1
Kang, DW Lee, YA
Lee, HL
Ahn, SK
Lee, HL
9.8
6.8
4.8
5.3
4.1
Won B
Lee, HL
Jeon, JH
Chae, SL
Chae, SL
5.9
4.5
4.2
4.5
3.7
Rain
Kang, DW
Rain
Song, HK
Kim, JE
5.6
3.8
4.0
4.4
3.4
Lee, NY
Song, HK Song, HK
Kim, JE
Ahn, SK
4.2
3.2
3.9
4.2
3.4
Lee, YA
Jang, DK Jang, DK
Jeon, JH
Kim, HJ
3.1
2.9
3.9
3.9
3.4
Lee, HL
Lee, NY
Kim, JE
Jang, DK
Kim, HA
3.1
2.9
3.5
3.9
2.6
Song, HK
Rain
Ahn, SK
Lee, HL
Song, HK
2.8
2.7
3.2
3.8
2.4
Kim C
Won B
Lee, MY
Jeon, IH
Ko, DS
2.8
2.7
2.6
2.9
2.2
Table 3-4 Preference Information about Celebrity Endorser from KOBACO
Humour
M10s
4.8
Tradition Children Consumer Animal Animation Celebrity Entertainer Foreign Star Sexual Comparison
Image
Product
Curiosity
/Humanism Entry
Entry Entry /Comic
Entry
Entry
Entry
Perception
Ad
Emphasis Ad Emphasis Ad
3.8
3.8
3.6
3.9
4.0
2.9
4.4
35
3.9
2.8
2.8
2.8
2.8
3.2
M20s
4.8
4.2
3.9
3.8
3.7
3.7
2.9
4.0
3.7
3.3
3.0
3.0
3.0
3.2
M30s
4.6
4.3
4.1
3.9
3.6
3.6
2.9
3.7
3.3
3.0
3.0
3.1
3.1
2.9
M40s
4.3
4.3
4.0
3.9
3.6
3.4
3.1
3.6
3.2
2.9
2.9
3.0
3.1
2.8
M50s
4.3
4.4
3.9
3.8
3.6
3.1
3.2
3.6
3.1
2.6
2.9
3.1
3.1
2.7
F10s
4.8
3.9
4.2
3.7
3.9
4.1
2.9
4.5
3.8
2.5
2.5
2.7
2.7
3.1
F20s
4.8
4.5
4.4
4.0
4.0
3.8
3.0
4.1
3.4
2.6
2.6
2.9
3.0
3.1
F30s
4.7
4.4
4.4
4.0
3.8
3.9
3.1
3.9
3.2
2.5
2.7
3.0
3.1
2.8
F40s
4.5
4.5
4.4
4.1
3.8
3.9
3.2
3.8
3.1
2.3
2.7
3.1
3.3
2.7
F50s
4.3
4.4
4.2
4.0
3.7
3.3
3.2
3.7
3.0
2.3
2.8
3.0
3.1
2.6
Table 3-5 Preference Information about Advertising Types from KOBACO
Drink Cookie Food Alcohol Household Cosmetic Car
Medical Home
Cell/Mobile Department
Study
Computer
Furniture Clothes Finance
Suuplies Appliance
Phone
Store
Book
M10s 4.0
4.1
3.9
2.8
2.8
2.5
3.4
2.6
3.0
4.3
4.7
3.0
2.4
3.5
2.1
2.3
M20s 3.6
3.4
3.5
3.6
3.1
3.0
4.2
3.0
3.5
4.2
4.5
3.2
2.7
3.6
2.8
2.2
M30s 3.3
3.2
3.3
3.5
3.0
2.7
4.3
3.2
3.4
3.9
4.1
3.0
2.7
3.0
3.1
2.5
M40s 3.3
3.1
3.3
3.5
3.0
2.8
4.0
3.5
3.4
3.6
3.8
3.0
2.7
3.0
3.2
2.6
M50s 3.2
3.0
3.1
3.4
3.0
2.7
3.7
3.5
3.3
3.0
3.4
2.9
2.7
2.9
3.0
2.2
F10s 4.1
4.3
3.9
2.9
3.8
3.9
3.1
2.7
3.2
4.1
5.0
3.5
2.9
4.3
2.4
2.7
F20s 3.8
3.8
3.7
3.4
4.0
4.5
3.6
3.2
3.8
3.7
4.5
3.7
3.3
4.3
3.0
2.6
F30s 3.6
3.5
3.7
3.3
3.9
4.1
3.6
3.6
4.1
3.7
4.0
3.7
3.4
3.9
3.4
3.5
F40s 3.5
3.5
3.6
3.2
3.9
4.0
3.5
3.7
4.0
3.6
3.7
3.6
3.4
3.7
3.4
3.0
F50s 3.2
3.1
3.4
2.9
3.7
3.7
3.1
3.6
3.9
2.9
3.2
3.4
3.1
3.4
3.0
2.0
Table 3-6 Preference Information about Advertising Items from KOBACO
Table 3-7 shows an example of the preference tables about celebrity endorser,
advertising types, and advertising items for M10s (Gender: Male and Age: 10~19).
Celebrity
Endorser
Jeon, JH
M10s
Advertising
Types
Humour
Advertising
Items
Cell/Mobile Phone
Kwon, SW
Entertainer Entry
Computer
Μ
Μ
Μ
Lee, NY
Image Ad
Study Book
Boa
Product Emphasis Ad
Finance
Table 3-7 Preference Table of Advertising Contents for M10s
36
The preference table of advertising contents for M10 consists of three columns of 11
celebrity endorsers, 14 advertising types, and 16 advertising items. In the preference
tables of advertising contents, each column is sorted in descending order by the
preference values. If the favorite celebrity endorser, advertising types, and advertising
items are set as a criterion, TV viewers would watch almost the same advertising
contents. In order to avoid this problem, it is required to make the criterion for selecting
suitable celebrity endorser, advertising types, and advertising items. In order to make a
choice of the celebrity endorser, advertising types, and advertising items, 24 hours are
divided into 4 parts on the basis of 6 hours, and the celebrity endorser, advertising types,
and advertising items are divided by TV viewer’s preferred TV viewing time.
Figure 3-9 shows how to divide celebrity endorser, advertising types, and advertising
items depending on the preferred TV viewing time.
24
1
4
Endorser – 1st ~ 3rd
Ad types – 1st ~ 4th
Ad items – 1st ~ 4th
Endorser – 10th ~ 11th
Ad types – 12th ~ 14th
Ad items – 13th ~ 16th
Endorser – 7th ~ 9th
Ad types – 9th ~ 11th
Ad items – 9th ~ 12th
Endorser – 4th ~ 6th
Ad types – 5th ~ 8th
Ad items – 5th ~ 8th
18
3
6
2
12
Figure 3-9 An Example of Classification of Celebrity Endorser, Advertising Types, and
Advertising Items based on the Preference of TV viewing time
37
The gray boxes in Figure 3-9 represent the order of the preferred TV viewing time.
In Figure 3-9, a time band from 18 to 24 is the favorite viewing time, and a time band
from 6 to 12 is the 2nd most preferred viewing time. 3 and 4 in the gray boxes are defined
in the same way. Therefore, the TAD service can be performed like the below.
• 0 ≤ Inference Ending Time Point < 6 Æ ‘4’ Advertisement
• 6 ≤ Inference Ending Time Point < 12 Æ ‘2’ Advertisement
• 12 ≤ Inference Ending Time Point < 18 Æ ‘3’ Advertisement
• 18 ≤ Inference Ending Time Point < 24 Æ ‘1’ Advertisement
For example, we assume that Table 3-7 is sorted by the preference values of
advertising contents, and the time after the profile inference result is between 18 and 24
o’clock. One of the celebrity endorsers ranked between 1st and 3rd, one of the advertising
types ranked between 1st and 4th, and one of the advertising items ranked between 1st and
4th are randomly selected. Then, we can make a query with the randomly selected
celebrity endorser, advertising type, and advertising item. The query is conditioned with
‘and.’ If there is a TVA metadata in the TVA metadata DB after retrieving with the
query, the CRID of the retrieved TVA metadata can provide advertising contents through
the location resolving with the CRID table. If there is no result of the query, we can make
a query that is linked as ‘or’ condition. After retrieval with the query in the TVA
metadata DB, there could be many retrieval results. In the case, we randomly select one
of them. Then, the CRID of the randomly selected TVA metadata is parsed, and real
advertisement content is sent to TV terminals after the location resolving procedure with
the CRID table.
38
4. Experiment and Implementation Result
In this chapter, we show various experimental results with implementation results
such as accuracy of the proposed user profile reasoning algorithm which is MSC, TVA
metadata of advertising contents, TVA metadata of TV program contents, user preference
metadata, UIT implementation results, HOS implementation results, and demonstration of
the TV personalization framework.
The assumption of the TV personalization framework is that family members have
their own UITs, and there are the DTTs, equipped with IP-STB, in the living room and
the kitchen. Since all family members have their own UITs, their private information is
not necessary to be open. Also, the family members can consume their preferred contents
anytime and anywhere through their own devices.
In the TV personalization framework, all entities such as HOS, UITs, and DTTs are
implemented in different operation systems. The HOS is implemented with Visual
C++7.1 and Window XP, and the DTT is realized with GCC (Gnu C Compiler) and
Wxwindow class in Linux RedHat 9.0, and the UIT is embodied in Windows Mobile
2003 2nd Edition with Embedded Visual C++4.0 and Pocket PC 2003 SDK (Software
Development Kit). Also, 42 TV program contents and 28 advertising contents were used
to test the proposed TV personalization framework. The TV program contents were
encoded by MPEG-2, broadcasted through four different TV channels, and divided into 8
different genres. And, the advertising contents were freely downloaded from NGTV
(http://www.ngtv.net) and advertising company web sites.
39
4.1. Experiment Result of User Profile Reasoning
The experiment for the profile reasoning algorithm is conducted with real TV
program viewing history from the AC Nielson Korea Research Center. The TV program
viewing history was recorded by 2522 people (Male: 1243 and Female: 1279) from Dec.
2002 to May. 2003. In order to perform an experiment, the TV program viewing history
data is divided into two groups such as training data and testing data. The training data is
randomly selected from 70% (1764 people) data out of the total TV program viewing
history, and the rest 30% (758 people) is used as the testing data. That is, the training data
is TV program viewing history data of 1764 people for 6 months, and the testing data is
TV program viewing history data of 758 people during 6 months. Also, for more accurate
experiment, we created 8 different pairs of the training and testing data.
The threshold values are CTh = 30 and TTh = 0.1 in order to extract feature values
from the training data. In other words, feature values in the feature vectors of the training
data are extracted on the basis of which the percentage of watching TV program is greater
than 10% ( TTh = 0.1 ). Satisfying with this viewing time condition, the number of
consumed TV programs are greater than 30 ( CTh = 30 ). Table 4-1 shows the
experimental result of the MSC, and the accuracy in Table 4-1 is the average accuracy of
the 8 different pairs of the training and testing data.
Gender and
Age Group
M0s
F0s
M10s
F10s
VC
76.69
67.14
66.89
67.76
Accuracy (%)
ED
71.96
67.86
71.28
68.75
40
MSC
88.21
89.28
89.29
65.79
M20s
60.72
62.95
86.50
F20s
68.18
73.58
78.49
M30s
63.69
72.42
78.80
F30s
63.15
66.88
76.17
M40s
59.82
64.51
86.59
F40s
69.71
64.83
72.25
M50s
54.86
64.58
82.86
F50s
60.86
60.20
67.91
M60s
65.76
67.94
89.77
F60s
56.90
50.86
86.61
Average
64.54
66.81
80.17
Accuracy
Table 4-1 Experimental Result of Multi-Stage Classifier (M: Male & F: Female)
In the table 4-1, VC is the vector correlation, and ED is the Euclidean distance, and
MSC is the multi-stage classifier. Also, M10s represents a group whose gender is male
and age is between 10 years old and 19 years old. To compare other methods with the
proposed user profile reasoning algorithm, we show the accuracy result of the vector
correlation and the Euclidean distance. From the table 4-1, the proposed MSC has 15%
higher accuracy than other two methods. The reason why the accuracy for F10s and F50s
is lower than other groups is that feature vectors for F10s and F50s have similar patterns
to other groups.
4.2. TVA Metadata of Advertising Contents
In this thesis, the TVA metadata for advertising contents are generated based on
ProgramInformationTable,
ServiceInformationTable,
and
CreditsInformationTable. Table 4-2 shows an example of TVA metadata for a
cellular phone advertising content ‘Samsung Anycall V420.’
<?xml version="1.0"?>
41
<TVAMain
xml:lang="ko"
publisher="Samsung
xmlns:mpeg7="urn:mpeg:mpeg7:schema:2001"
Anycall"
xmlns="urn:tva:metadata:2002"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="urn:tva:metadata:2002 E:\MajorStudy\Standard\TV-Anytime\TV-AnytimeSchema\
tva_metadata_v13.xsd">
<!—Ad Content Provider -->
<CopyrightNotice>Copyright (c) Samsung Anycall 2005</CopyrightNotice>
<ProgramDescription>
<ProgramInformationTable>
<!-- CRID of Ad Content-->
<ProgramInformation programId="crid://www.samsung.com/AnycallV420">
<BasicDescription>
<Title type="main">Samsung Anycall v420</Title> <!—Title of Ad Content -->
<MediaTitle> <!—Media Title Address of Ad Content -->
<mpeg7:TitleImage> <mpeg7:MediaUri>
http://mccb.icu.ac.kr/metadata/picture/AnycallV420.bmp
</mpeg7:MediaUri></mpeg7:TitleImage>
</MediaTitle>
<Synopsis>Sexual Perception</Synopsis><!-- Ad Type of Ad Content -->
<Keyword>Lee, HL</Keyword><!-- Ad Model of Ad Content -->
<Keyword>Sexual Perception</Keyword><!-- Ad Type of Ad Content -->
<Keyword>Cell/Mobile Phone</Keyword><!-- Ad Item of Ad Content -->
<Keyword> Samsung Anycall </Keyword><!-- Ad Content Provider -->
<!-- Ad Item of Ad Content -->
<Genre href="urn:tva:metadata:cs:ContentCommercialCS:2002:3.50.5.7">
<Name>Cell/Mobile Phone</Name>
</Genre>
<!-- Ad Model of Ad Content -->
<CreditsList>
<CreditsItem role="urn:tva:metadata:cs:TVARoleCS:2002:V709">
<PersonNameIDRef ref="credit_1"/>
<Character> <mpeg7:GivenName>Ad Model</mpeg7:GivenName> </Character>
</CreditsItem>
</CreditsList> </BasicDescription>
</ProgramInformation>
</ProgramInformationTable>
<ServiceInformationTable>
<ServiceInformation serviceId="Ch2">
<Name>v420</Name> <!-- Real Product Name of Ad Content -->
<Owner>Samsung Anycall</Owner> <!-- Company Name of Ad Content Provider -->
</ServiceInformation>
42
</ServiceInformationTable>
<CreditsInformationTable>
<PersonName personNameId="credit_1">
<!-- Ad Model’s Name of Ad Content -->
<mpeg7:GivenName>Lee, HL</mpeg7:GivenName>
</PersonName>
</CreditsInformationTable>
</ProgramDescription> </TVAMain>
Table 4-2 An Example of TVA Metadata for an Advertising Content
In Table 4-2, the information about an advertising content provider is inserted in
<CopyrightNotice> under <TVAMain>. We can write general information about
the advertising content in <ProgramInformationTable>. By utilizing ‘programId’
attribute in <ProgramInformation>, the CRID is defined to refer to the advertising
content. By defining the advertising types in <Synopsis>, the advertising types can be
obtained from <Synopsis>, and the information of the advertising items is in
<Genre>. Also, <CreditsList> includes how many celebrity endorsers are in the
advertisement content. In <Keyword>, the advertising content provider, celebrity
endorser, advertising type, and advertising item are defined for keywords-based retrieval.
<ServiceInformationTable> contains an advertising content provider name and a
product name. In <CreditsInformationTable>, we write about the name of the
celebrity endorser in <CreditsList>.
4.3. TVA Metadata of TV Program Contents
In this section, we show TVA metadata instances which are used for describing TV
program
contents.
We
utilize
43
ProgramInformationTable,
GroupInformationTable,
Description
and
CreditsInformationTable
metadata, ServiceInformationTable in
Instance
in
Content
Description
metadata, and SegementInformationTable in Segmentation metadata to retrieve
and consume contents in the TV personalization framework.
In the ProgramInformationTable, general information about a piece of TV
program contents can be described as shown in Table 4-3.
<ProgramInformationTable>
<ProgramInformation programId="crid://www.mbc.co.kr/3.1.3.2_MBCDiscussionFor100mins_01">
<BasicDescription>
<Title type="seriesTitle">MBC Discussion for 100 Mins</Title>
<MediaTitle>
<mpeg7:TitleImage> <mpeg7:MediaUri>
http://mccb.icu.ac.kr/metadata/picture/3.1.3.2_MBCDiscussionFor100mins.bmp
</mpeg7:MediaUri> </mpeg7:TitleImage>
</MediaTitle>
<Synopsis>What are the solutions of Korean Navies Skirmish in Yellow Sea?</Synopsis>
<Keyword>Korean Navies Skirmish in Yellow Sea</Keyword>
<Keyword>Son, S.H.</Keyword> <Keyword>Discussion</Keyword>
<Genre href="urn:tva:metadata:cs:ContentCS:2002:3.1.3.2"> <Name>Social</Name> </Genre>
<ParentalGuidance>
<mpeg7:ParentalRating href="urn:tva:metadata:cs:IntendedAudienceCS:2002:4.2.4">
<mpeg7:Name>All Ages</mpeg7:Name>
</mpeg7:ParentalRating>
</ParentalGuidance>
<Language type="original" supplemental="false">ko</Language>
<CaptionLanguage closed="true" supplemental="false">ko</CaptionLanguage>
<SignLanguage>ko</SignLanguage>
<CreditsList>
<CreditsItem role="urn:tva:metadata:cs:TVARoleCS:2001:V486">
<PersonNameIDRef ref="credit_1"/>
<Character> <mpeg7:GivenName>Director</mpeg7:GivenName> </Character>
</CreditsItem>
<CreditsItem role="urn:tva:metadata:cs:TVARoleCS:2001:V42">
<PersonNameIDRef ref="credit_2"/>
44
<Character> <mpeg7:GivenName>Announcer</mpeg7:GivenName> </Character>
</CreditsItem>
</CreditsList>
<ProductionDate> <TimePoint>2002</TimePoint> </ProductionDate>
<ProductionLocation>ko</ProductionLocation>
<ReleaseInformation>
<ReleaseDate> <DayAndYear>2002-07-11</DayAndYear> </ReleaseDate>
<ReleaseLocation>ko</ReleaseLocation>
</ReleaseInformation>
</BasicDescription>
<MemberOf crid="crid://www.mbc.co.kr/3.1.3.2_MBCDiscussionFor100mins"/>
</ProgramInformation>
</ProgramInformationTable>
Table 4-3 Metadata Instance of the ProgramInformationTable in TV Anytime
The GroupInformationTable is used for describing a group of the TV
program contents, a type of the group, and information of the group. Table 4-4 shows an
example of the usage of the GroupInformationTable.
<GroupInformationTable>
<GroupInformation groupId="crid://www.mbc.co.kr/3.1.3.2_MBCDiscussionFor100mins">
<GroupType xsi:type="ProgramGroupTypeType" value="series"/>
<BasicDescription>
<Title type="seriesTitle"> MBC Discussion for 100 Mins </Title>
<MediaTitle>
<mpeg7:TitleImage> <mpeg7:MediaUri>
http://mccb.icu.ac.kr/metadata/picture/3.1.3.2_MBCDiscussionFor100mins.bmp
</mpeg7:MediaUri> </mpeg7:TitleImage>
</MediaTitle>
<Synopsis>Discussion program about social issues with specialists </Synopsis>
<Genre href="urn:tva:metadata:cs:ContentCS:2002:3.1.3.2"> <Name>Social</Name> </Genre>
<ParentalGuidance>
<mpeg7:ParentalRating href="urn:tva:metadata:cs:IntendedAudienceCS:2002:4.2.4">
<mpeg7:Name>All Ages</mpeg7:Name>
</mpeg7:ParentalRating>
</ParentalGuidance>
</BasicDescription>
45
</GroupInformation>
</GroupInformationTable>
Table 4-4 Metadata Instance of the GroupInformationTable in TV Anytime
As shown in Table 4-5, the information about TV program content provider is
described in the ServiceInformationTable.
<ServiceInformationTable>
<ServiceInformation serviceId="Ch2">
<Name>MBC</Name>
<Owner>MBC</Owner>
</ServiceInformation>
</ServiceInformationTable>
Table 4-5 Metadata Instance of the ServiceInformationTable in TV Anytime
In the SegmentInformationTable, the detailed information of the TV program
contents such as event, sub-title, synopsis, and background can be defined. Table 4-6
gives an example of the usage of the SegmentInformationTable. The segment
information can be divided into 2 types such as SegmentGroupList and
SegmentList. In the SegmentList, detailed information of a piece of TV program
contents can be described on the basis of a segment unit. In the SegmentGroupList,
we can group similar segments and define the grouped segments. Since the segment
information
is
too
long,
we
have
omitted
description
of
the
SegmentInformationTable in Table 4-6. Detailed example of TVA metadata for a
piece of TV program contents is given in Appendix A.
<SegmentInformationTable>
<SegmentList>
<SegmentInformation
segmentId="segid.--introduction1_0_1457crid.--www.mbc.co.kr-3.1.3.2_MBCDiscussionFor100mins_01">
<ProgramRef crid="crid://www.mbc.co.kr/3.1.3.2_MBCDiscussionFor100mins_01"/>
46
<Description>
<Title xml:lang="ko">Introduction to today’s title</Title>
<Synopsis xml:lang="ko">
MBC Discussion for 100 Mins - What are the solutions of Korean Navies Skirmish in Yellow Sea
</Synopsis>
<Keyword xml:lang="ko">Synopsis</Keyword>
<Keyword xml:lang="ko"> Korean Navies Skirmish in Yellow Sea </Keyword>
<Keyword xml:lang="ko">Solution</Keyword>
</Description>
<SegmentLocator>
<MediaRelIncrTimePoint mediaTimeUnit="PT1N30F">0</MediaRelIncrTimePoint>
<MediaIncrDuration mediaTimeUnit="PT1N30F">1457</MediaIncrDuration>
</SegmentLocator>
<KeyFrameLocator>
<MediaRelIncrTimePoint mediaTimeUnit="PT1N30F">0</MediaRelIncrTimePoint>
</KeyFrameLocator>
</SegmentInformation>
..... (Omitted)
</SegmentList>
<SegmentGroupList>
<SegmentGroupInformation
topLevel="false"
groupId="segroupid.--GID_1_1"
ordered="1"
numberOfKeyFrames="1">
<ProgramRef crid="crid://www.mbc.co.kr/3.1.3.2_MBCDiscussionFor100mins_01"/>
<GroupType xsi:type="SegmentGroupTypeType" value="tableOfContents"/>
<Description>
<Title xml:lang="ko">Introduction to today’s title</Title>
<Synopsis xml:lang="ko">
MBC Discussion for 100 Mins - What are the solutions of Korean Navies Skirmish in Yellow Sea
</Synopsis>
<Keyword xml:lang="ko">Synopsis</Keyword>
<Keyword xml:lang="ko"> Korean Navies Skirmish in Yellow Sea </Keyword>
<Keyword xml:lang="ko">Solution</Keyword>
</Description>
<Segments
refList="segid.--introduction1_0_1457crid.--www.mbc.co.kr-3.1.3.2_MBCDiscussionFor100mins_01"/>
<KeyFrameLocator>
<MediaRelIncrTimePoint mediaTimeUnit="PT1N30F">0</MediaRelIncrTimePoint>
</KeyFrameLocator>
</SegmentGroupInformation>
.... (Omitted)
47
<SegmentGroupInformation
topLevel="true"
groupId="segroupid.--GID_13"
ordered="1"
numberOfKeyFrames="1">
<ProgramRef crid="crid://www.mbc.co.kr/3.1.3.2_MBCDiscussionFor100mins_01"/>
<GroupType xsi:type="SegmentGroupTypeType" value="tableOfContents"/>
<Description>
<Title xml:lang="ko">Summary of Discussion and Ending </Title>
<Synopsis xml:lang="ko"> Summary of Discussion and Ending </Synopsis>
<Keyword xml:lang="ko">Summary of Discussion</Keyword>
<Keyword xml:lang="ko">Ending</Keyword>
</Description>
<Groups refList="segroupid.--GID_13_1segroupid.--GID_13_2"/>
<KeyFrameLocator>
<MediaRelIncrTimePoint mediaTimeUnit="PT1N30F">154801</MediaRelIncrTimePoint>
</KeyFrameLocator>
</SegmentGroupInformation>
</SegmentGroupList>
</SegmentInformationTable>
Table 4-6 Metadata Instance of the SegmentInformationTable in TV Anytime
4.4. User Preference Metadata
In the TV personalization framework, the HOS automatically computes user
preference values of TV program genres through the user preference learning algorithm
and creates user preference metadata based on the computed preference values. The user
preference metadata is specified by User Preference DS in MDS of MPEG-7 Part 5 [10].
The user preference learning algorithm computes the preference values of TV program
genres on the basis of the time, date, and a day of week. The algorithm based on the
Bayesian Network adjusts weighting values by the time from TV program viewing
history data [14]. Table 4-7 is an example of user preference metadata.
<?xml version="1.0" encoding="UTF-8"?>
<Mpeg7xmlns="urn:mpeg:mpeg7:schema:2001" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="urn:mpeg:mpeg7:schema:2001 D:\Mpeg7-2001.xsd">
48
<Description xsi:type="UserDescriptionType">
<UserPreferences>
<FilteringAndSearchPreferences>
<ClassificationPreferences>
<Genre preferenceValue="0"><Name>Education</Name></Genre>
<Genre preferenceValue="43"><Name>DramaMovie</Name></Genre>
<Genre preferenceValue="0"><Name>News</Name></Genre>
<Genre preferenceValue="1"><Name>Sports</Name></Genre>
<Genre preferenceValue="0"><Name>Children</Name></Genre>
<Genre preferenceValue="56"><Name>Entertainment</Name></Genre>
<Genre preferenceValue="0"><Name>Information</Name></Genre>
<Genre preferenceValue="0"><Name>etc</Name></Genre>
</ClassificationPreferences></FilteringAndSearchPreferences>
</UserPreferences>
</Description>
</Mpeg7>
Table 4-7 Instance of User-Preference Metadata
Figure 4-1 shows the result of the generated pEPG metadata from the user preference
metadata created from the user preference learning algorithm. Also, Figure 4-2 presents
the result of keywords based retrieval (Title: Ages, Synopsis: Drama, Character: Lee,
N.Y.). The retrieval result is also created in the same way of pEPG metadata generation.
Figure 4-1 pEPG Creation based on the User Preference Metadata
49
Figure 4-2 EPG Creation based on the Keyword-based Retrieval
4.5. User Information Terminal Implementation Result
Figure 4-3 presents the implementation result of the UIT. The UIT is implemented
on PDA and Smart Phone, and the UIT is able to communicate with the HOS via the
wireless network.
(a) Start Screen
(b) Log-in Screen
(d) Device Selection
(e) pEPG Browser
50
(c) TAD Screen
(f) Retrieval Screen
(g) Retrieval Result
(h) Program Selection
(j) Segments of (h)
(k) pEPG Remocon
(i) pEPG Player
(l) Content Interceptor
Figure 4-3 Implementation Results of User Information Terminal
Figure 4-3 (a) is the start screen of a UIT, and when a user clicks the picture in the
middle, the UIT connects to the HOS. Figure 4-3 (b) is the log-in screen, and the user
inputs his/her ID and password. After the user logs into the HOS, the HOS automatically
runs the user profile reasoning algorithm and advertising content selection method. The
selected advertising is transmitted to the UIT, and the user consumes an advertising
content as shown in Figure 4-3 (c). After consuming advertising contents, the user selects
where to consume a piece of TV program contents as shown in Figure 4-3 (d). Figure 4-3
(e) shows the parsing result of pEPG metadata like Figure 4-1 through the TVA metadata
51
parser. The parsing result is displayed on the pEPG browser. Figure 4-3 (f) is keywordbased retrieval screen. There are 3 kinds of keyword categories such as title, synopsis,
and a character. Those keywords are conditioned by ‘and’ or ‘or’. Figure 4-3 (g) displays
keyword-based retrieval result based on the keywords of Figure 4-3 (f). When a user
requests retrieval to the HOS, the HOS generates the retrieval results like Figure 4-2.
Then, the generated retrieval result is transmitted to the UIT. The UIT pares the retrieval
results and displays the results on the pEPG browser. Figure 4-3 (h) is displayed after the
user clicks a picture of a TV program content in the list of Figure 4-3 (g). When the user
clicks the picture in the bottom of the left corner, the CRID of the content is sent to the
HOS. Next, the HOS generates segment metadata of the requested CRID and transmits
the segment metadata to the UIT. Then, the UIT parses the segment metadata and
displays the parsing result of the metadata. Figure 4-3 (i) is the pEPG player and is
occurred when the user clicks the play button in Figure 4-3 (h). The user can consume the
selected content with the user’s UIT when the user selects UIT as consuming terminal in
Figure 4-3 (d). While the content is playing, the user can perform the content mobility by
clicking ‘L’ for a DTT in the living room or ‘K’ for a DTT in the kitchen. Figure 4-3 (j) is
the segments list of Figure 4-3 (h) program, and Figure 4-3 (k) is the pEPG remocon and
is shown when the user selects one of DTTs in Figure 4-3 (d). With the remocon, the user
can control a player in the DTT. Also, the content mobility can be done by pressing one
of the 3 bottom buttons. Figure 4-3 (l) is the request of the content interceptor. The
message box for the content interceptor is popped up when the user selects a DTT on
which a piece of contents is playing. Then, the session information of the content which
52
is rendering on the selected DTT is transmitted to the HOS when the user selects ‘YES’ in
the message box. The transmitted session information is saved in the session information
history of the user who requested the content interceptor in the HOS.
4.6. Home Server Implementation Result
The HOS has a DB to store TV program contents and TVA metadata, a transcoding
module to adaptively transcode contents based on terminal characteristics, a content
streaming module, a user preference learning module to automatically compute user
preference values, and a TAD module to infer user’s profile (gender and ages) and to
select an advertising content for the target user.
There are 4 roles in the HOS. First, it manages and stores contents from various
sources such as terrestrial, satellite, and cable broadcasting and TVA metadata of the
contents. Second, it receives requests such as a piece of contents or content mobility from
the user who logged into the HOS with a UIT. Also, it understands the requests and sends
commands to terminal devices such as a UIT or a DTT. Third, the HOS periodically
generates and updates user preference values by storing and analyzing the TV program
viewing history of contents. Lastly, the HOS is responsible for TAD service. Figure 4-4
shows contents of message flows among the HOS, UITs, and DTTs.
53
Figure 4-4 Implementation Result of the Home Server
4.7. Demonstration
Figure 4-5 shows the demonstration of the proposed TV personalization framework
in the thesis. The scenario of the demonstration is as follows.
„
Entities of the proposed framework demonstration are a HOS, two DTTs in the living
room and the kitchen, and UITs for user A and user B. Figure 4-5 (a) shows the
entities and the HOS is running and waiting requests from other terminals such as
DTTs and UITs in the different place.
„
When user A and B log into the HOS, the HOS infers the users’ profile by the user
profile reasoning algorithm. Based on the estimated profiles for user A and B, the
54
HOS selects advertising contents for user A and B. User A and B are consuming
advertising contents as shown in Figure 4-5 (b) and (c).
„
After watching advertising contents, user A and B are consuming their preferred
contents, recommended by user preference learning algorithm in the HOS, on
different DTTs as shown in Figure 4-5 (d). Figure 4-5 (e) and (f) show the screens of
UITs for user A and B. A while later, user B thinks user A’s content is interesting.
Then, user B stops consuming his/her content and joins watching TV with user A in
the living room.
„
Suddenly user B consuming a piece of contents with user A in the living room comes
up user B’s mind that user B needs to do something important. So, user B can not
watch TV with user A anymore. However, user B wants to consume the content
which user B were watching with user A in minutes.
„
With user B’s UIT, user B utilizes the content interceptor to intercept the session
information of the content currently playing in the living room without interrupting
user A. The request of the content interceptor is shown in Figure 4-5 (g).
„
After user B is done with his/her job, user B consumes the intercepted content in the
kitchen because user A is still watching TV in the living room. Figure 4-5 (h) shows
the result of the content mobility for multi-users that user A and B are consuming the
same content at the different places and times.
55
Kitchen
TV
User B UIT
(HP iPAQ hx4700)
User A UIT
(Samsung MITs m4300)
Living
TV
(a) Entities of the Demonstration System
(The HOS is running and waiting a network message in the different place)
(b) TAD Service for User B
(c) TAD Service for User A
56
(d) Playing Contents in different places
(e) User B Screen
(f) User A Screen
57
(g) Request Content Interceptor
(h) Content Mobility for Multi-Users
Figure 4-5 Demonstration of the Proposed TV Personalization Framework
58
5. Conclusion and Future Works
A paradigm shift is expected to be made from the traditional broadcasting service as
mass media to user-oriented broadcasting service as personal media due to the rapid
growth of Internet, broadcasting and communications convergence, digital technologies,
etc. Also, the digital TV and the digital technologies have been rapidly applied to digital
home environment under the digital convergence. Under the digital home environment, it
is expected that while different users are consuming a piece of contents on a specific
DTT, the users can seamlessly consume the content on their own terminals. Also, based
on the user preferences, the users can access and consume their preferred TV program
contents or advertising contents in which the users might be interested on a specific DTT
or their own UITs. Therefore, in this thesis, we design TV personalization framework for
personalized TV service under the digital home environment. In the thesis, there are two
major services such as TAD service and seamless content mobility service for multiusers.
User profile reasoning algorithm and advertising content selection method are
applied for the TAD service. The user profile reasoning algorithm predicts unknown TV
viewer’s gender and ages by analyzing TV program viewing history. Based on the
estimated user’s gender and ages, advertising content selection method is performed with
TVA metadata of advertising contents and preference values about celebrity endorsers,
advertising items, and advertising types from KOBACO. The accuracy of the user profile
reasoning algorithm is about 80% which is higher than other methods.
59
The other service in the TV personalization framework is the seamless content
mobility service for multi-users. The content mobility service in the TV personalization
framework is designed for the seamless contents consumption based on user preference
via various kinds of user terminals. For the designed seamless content mobility service,
we utilize the TVA Metadata to describe TV program and advertising contents, TVA
Content Referencing to refer to a piece of contents, and MPEG-21 DIA tool to describe
the context information for user environments, user terminal characteristics, user
characteristics for universal access and consumption of the contents. Also, the content
mobility service is designed to make it possible to seamlessly consume contents by a
single user or multi-users among various kinds of user terminals.
To test the TV personalization framework, we showed a plenty of experimental
results such as accuracy of the user profile reasoning algorithm, TVA metadata of
advertising contents and TV program contents, metadata of user preference,
implementation results of UIT and HOS, and demonstration of the TV personalization
framework.
With the plenty of experimental results, we validated the proposed TV
personalization framework which enables multi-users to consume their preferred TV
program contents anytime and anywhere through any devices by the seamless content
mobility service and to receive TAD service by the user profile reasoning algorithm and
the advertising content selection method.
For the continuity of our work, we are planning a DTT to be equipped with IP-STB
after embodying functionalities of the current DTT into the IP-STB, and real-time
60
transconding engine will be incorporated in the TV personalization framework. We will
also study how to improve the accuracy of our user profile reasoning algorithm and
develop more effective advertising content selection method which automatically
considers various user preferences of advertising contents.
61
국문 요약
석사학위논문
개인화 TV 프레임워크에서 표적 광고를 위한 사용자
프로파일 추론에 관한 연구
공학부 김문조
지상파, 위성파, 케이블 방송 같은 기존의 방송환경은 시청자 취향에 상관없이
일방적인 단 방향 방송 서비스를 제공해왔다. 하지만, 최근에는 광대역 통신망을 통한
다양한 미디어 전송이 가능하게 되었다. 또한, 방송 환경에서 양방향 통신이 가능하게
됨으로써 장르, 시청 시간대, 배우 등 시청자의 선호도를 반영한 개인화 TV 서비스가
대두되고 있다. 따라서, 콘텐츠 제공 서비스 측면에서 기존의 방송환경인 매스
미디어(mass media) 뿐만 아니라, 개인 미디어가 아주 중요한 요소가 되었다. 개인
미디어는 개개인의 선호도를 고려한 콘텐츠를 의미하는데, 개인화를 고려한 미디어
서비스를 제공하기 위해서는 개인화 TV 프레임워크와 이와 관련된 요소 기술들이
필수적이라고 할 수 있다.
본 논문에서는, 사용자 선호도에 기반한 개인화 전자프로그램 가이드(pEPG:
personalized Electronic Program Guide) 생성, 표적 광고 서비스, 그리고 콘텐츠 소비를
위한
콘텐츠
이동성
서비스(content
mobility
service)를
포함하는
개인화
TV
프레임워크를 제안한다. 제안된 개인화 TV 프레임워크는 다중 사용자를 위한 콘텐츠
이동성 서비스 기반 하에, 표적 광고 서비스(target advertisement service)의 사용자
프로파일 추론 알고리즘이 개발되었다.
사용자 프로파일 추론 알고리즘은 시청자의 TV 시청 데이터(TV program viewing
history data) 분석을 통하여 특정 시청자의 성별과 연령대를 예측한다. 예측된
시청자의 성별 및 연령대를 바탕으로 표적 광고 서비스가 이루어진다. 즉, 표적
광고는 사용자 프로파일 추론 알고리즘을 기반으로 한 광고 서비스이다.
제안된 개인화 TV 프레임워크내의 pEPG는 사용자 선호도 추론 알고리즘을
통하여 추론된 사용자 선호도 값을 기반으로 생성이 된다. 따라서, 사용자는 자신의
62
선호도 기반으로 생성된 pEPG를 통해서 자신이 선호하는 TV 프로그램 콘텐츠를
검색, 선택, 소비가 가능하게 된다.
또한, 개인화 TV 프레임워크내에서 사용자 선호도에 맞게 다양한 사용자 단말을
통하여 콘텐츠 이동이 가능하다. 콘텐츠 이동성 서비스를 위하여 TV Anytime
메타데이터는 콘텐츠에 대한 상세한 정보 기입과 소비를 위해서 기술되었고, MPEG-21
디지털 아이템 적응(DIA: Digital Item Adaptation) 도구는 범용적인 콘텐츠 접근 및
소비를
위한
정보(context
이동성(session
사용자
환경,
information)를
mobility)와
사용자
단말
기술하기
같은
특성,
위하여
개념인
콘텐츠
사용자
특성에
사용되었다.
이동성은
대한
컨텍스트
MPEG-21의
단일
사용자나
세션
다중
사용자들이 다양한 사용자 단말에서 콘텐츠를 끊임없이 소비하는 것을 가능하게
한다.
이러한 개인화 TV 프레임워크는 홈 서버, 디스플레이 TV 단말, 사용자 정보
단말들로 이루어져 있으며, 이들 구성원들을 통하여 사용자는 표적 광고 서비스와
콘텐츠 이동성 서비스가 가능하게 된다. 또한, 개인화 TV 프레임워크는 TV Anytime과
MPEG-21 DIA 표준화를 기반으로 이루어져 있다. 제안된 개인화 TV 프레임워크의
유용성과 효율성을 검증하기 위하여, 본 논문은 실제 TV 시청 데이터, 28개의 광고
콘텐츠, 4개의 다른 채널에서 방송된 8개의 장르로 이루어져 있는 42개의 TV
프로그램 콘텐츠를 이용하여 다양한 실험 결과를 보여준다.
63
References
[1] l. Popescu, “Supporting Multimedia Session Mobility using SIP,” Communication
Networks and Services Conference, Moncton, New Brunswick, Canada, pp.
122~123, May 15-16, 2003
[2] H. Schulzrinne, and E. Wedlund, “Application Layer Mobility using SIP,” ACM
Mobile Computing and Communications Review, vol.4, no.3, pp. 47~57, July 2000.
[3] H. Jeong, J.Y. Lim, Q. Shahab, Hendry and M.C. Kim, "Pervasive Multimedia via an
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[7] TV Anytime Forum, “Metadata,” S-3v1.3, Part-A: Metadata Schemas, Dec. 2002.
[8] TV Anytime Forum, “Content Referencing,” S-4v1.2, June 2002.
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ISO/IEC 21000-7 FDIS, N6168/MPEG67, Hawaii, USA, Dec. 2003.
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64
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023022-1 ~ 023022-10, April ~ June 2005.
65
Appendix A
An example of TVA metadata about TV program content ‘YunDohyun’s Love Letter’
<?xml version="1.0"?>
<!-- edited with XMLSPY v5 U (http://www.xmlspy.com) by MCCB (Information and Communications University) -->
<TVAMain xml:lang="ko" publisher="KBS" xmlns="urn:tva:metadata:2004"
xmlns:mpeg7="urn:mpeg:mpeg7:schema:2001" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="urn:tva:metadata:2004 E:\MajorStudy\Standard\TV-Anytime\TVAnytimeSchema\tva_metadata_v13_am1.xsd">
<CopyrightNotice>Copyright (c) 2005 KBS</CopyrightNotice>
<ProgramDescription>
<ProgramInformationTable>
<ProgramInformation programId="crid://www.kbs.co.kr/3.3.11_YunDohyunLoveLetter_01">
<BasicDescription>
<Title type="seriesTitle">윤도현의러브레터</Title>
<MediaTitle>
<mpeg7:TitleImage>
<mpeg7:MediaUri>
http://mccb.icu.ac.kr/metadata/picture/3.3.11_YunDohyunLoveLetter_01.bmp
</mpeg7:MediaUri>
</mpeg7:TitleImage>
</MediaTitle>
<Synopsis>
비, MC THE MAX, 전인권, 이현도+김창렬+에픽하이+주석+바스코+스퀘어
</Synopsis>
<Keyword>라이브</Keyword>
<Keyword>음악</Keyword>
<Keyword>윤도현</Keyword>
<Keyword>김제동</Keyword>
<Keyword>러브레터</Keyword>
<Genre href="urn:tva:metadata:cs:ContentCS:2002:3.3.11">
<Name>Music</Name>
</Genre>
<ParentalGuidance>
<mpeg7:ParentalRating href="urn:tva:metadata:cs:IntendedAudienceCS:2002:4.9.5">
<mpeg7:Name>Family with Children 8-15</mpeg7:Name>
</mpeg7:ParentalRating>
</ParentalGuidance>
<Language type="original" supplemental="false">ko</Language>
<CaptionLanguage closed="true" supplemental="false">ko</CaptionLanguage>
<SignLanguage>ko</SignLanguage>
<CreditsList>
<CreditsItem role="urn:tva:metadata:cs:TVARoleCS:2001:V709">
<PersonNameIDRef ref="credit_1"/>
<Character>
<mpeg7:GivenName>진행자1</mpeg7:GivenName>
</Character>
</CreditsItem>
<CreditsItem role="urn:tva:metadata:cs:TVARoleCS:2001:V709">
<PersonNameIDRef ref="credit_2"/>
<Character>
<mpeg7:GivenName>진행자2</mpeg7:GivenName>
</Character>
</CreditsItem>
<CreditsItem role="urn:tva:metadata:cs:TVARoleCS:2001:V106">
66
<PersonNameIDRef ref="credit_3"/>
<Character>
<mpeg7:GivenName>가수1</mpeg7:GivenName>
</Character>
</CreditsItem>
<CreditsItem role="urn:tva:metadata:cs:TVARoleCS:2001:V106">
<PersonNameIDRef ref="credit_4"/>
<Character>
<mpeg7:GivenName>가수2</mpeg7:GivenName>
</Character>
</CreditsItem>
<CreditsItem role="urn:tva:metadata:cs:TVARoleCS:2001:V106">
<PersonNameIDRef ref="credit_5"/>
<Character>
<mpeg7:GivenName>가수3</mpeg7:GivenName>
</Character>
</CreditsItem>
<CreditsItem role="urn:tva:metadata:cs:TVARoleCS:2001:V106">
<PersonNameIDRef ref="credit_6"/>
<Character>
<mpeg7:GivenName>가수4</mpeg7:GivenName>
</Character>
</CreditsItem>
</CreditsList>
<ProductionDate>
<TimePoint>2005</TimePoint>
</ProductionDate>
<ProductionLocation>ko</ProductionLocation>
<ReleaseInformation>
<ReleaseDate>
<DayAndYear>2005-01-07</DayAndYear>
</ReleaseDate>
<ReleaseLocation>ko</ReleaseLocation>
</ReleaseInformation>
</BasicDescription>
<MemberOf crid="crid://www.kbs.co.kr/3.3.11_YunDohyunLoveLetter"/>
</ProgramInformation>
</ProgramInformationTable>
<GroupInformationTable>
<GroupInformation groupId="crid://www.kbs.co.kr/3.3.11_YunDohyunLoveLetter">
<GroupType xsi:type="ProgramGroupTypeType" value="series"/>
<BasicDescription>
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75
Acknowledgement
I would like to thank everyone who has made this thesis possible because this thesis
would not have been presented without their supports. First of all, I would love to thank
my advisor Professor Munchurl Kim, who has given me a lot of advice and has taught me
what research works are and how to carry out the research works. During my master
study, he has trained me how to manage and take care of a lot of works at the same time.
Also, I would like to thank my thesis committee members, Professor Yongman Ro for
giving his valuable comments on my thesis and Professor Changick Kim for his keen
advice and encouragement comments on this thesis.
I would like to show my thankful mind to Professor Sanggil Kang and Professor
Kyung-Ae Cha for giving many advices and informative comments on my papers and
research works.
I would like to thank MCCB Lab members and Multimedia Track for their advice
and care, especially Jeongyeon Lim for her comments and advices on all my papers and
research works, Chanseok Yang for his great supports on Samsung project, Bumshik Lee
for his helps for SmarTV project, Hendry for giving informative resources on Samsung
projects, Hankyu Lee, Joonsik Choi, Yongjun Chang, Houari, Danu, Agus, Wonsang
You, Peng, and Ha.
I would love to show my appreciation to Youngsuk Kim, Duckyeon Kim, Kyongsok
Seo, and Junho Cho for their encouragements, kindness and care during my master study.
Also, my girlfriend Mikyung Kim is another one for receiving my thanks due to her care,
supports, and encouragement while I was struggling with works.
Lastly but most importantly, I deeply appreciate my parents’ supports and care
during my master study because they have done anything for me. I can say that without
any hesitation “어머니, 아버지 정말 고맙습니다! 부모님 덕분에 제가 여기 이렇게
까지 올 수 있었습니다. 멋진 아들로 키워 주셔서 고맙습니다. 제가 효도할 수 있도록
항상 건강하세요.”
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Biographical Sketch
Munjo Kim was born in Busan, Korea. He received the B.S degree in Mechatronics
Engineering from the TongMyong University in February 2004. After that, he started his
master study in the Engineering school of Information and Communications University
(ICU) in 2004. During the study, he worked for the research internship at
Telecommunication R&D Center in Samsung Electronics (2005.3.1 ~ 2005. 8.31). He
also participated in research projects on “A Study on Intelligent Agent and Metadata
Management Technology,” “A Study on Testbed of Smart Phone Application Program
based on Embedded Linux,” “A Study on Intelligent Agent Technology,” and “A Study
on Intelligent User Information Terminal Technology for Digital Home Application.”
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Publications
Journal Papers (Domestic)
1.
김문조, 이범식, 임정연, 김문철, 이희경, 이한규, “시청자 프로파일 추론과 TV
Anytime 메타데이터를 이용한 표적 광고,” 심사 중, 정보과학회 논문지, 2006.
2.
김문조, 양찬석, 임정연, 김문철, 박성진, 김관래, 오윤제, “TV Anytime 및 MPEG21 DIA 기반 콘텐츠 이동성을 이용한 디지털 홈 환경에서의 유비쿼터스 TV
콘텐츠 소비,” 한국방송공학회 논문지, 제10권, 제4호, 2005.
3.
김문조, 임정연, 강상길 김문철, 강경옥, "시청자 프로화일 추론 기법을 이용한
표적광고 서비스," 한국방송공학회 논문지, 제10권, 제1호, pp. 43 ~ 56, 2005.
Conference Papers (International)
1.
Munjo Kim, Bumshik Lee, Jeongyeon Lim, Munchurl Kim, Heekyung Lee, and Hankyu
Lee, “Target Advertisement System based on a TV Viewer’s Profile Reasoning,” submitted
to EuroITV 2006.
2.
Munjo Kim, Chanseok Yang, Jeongyeon Lim, Munchurl Kim, Sungjin Park, Kwanlae Kim,
and Yunje Oh, “TV Anytime and MPEG-21 DIA based Seamless Content Mobility Prototype
System for Digital Home Applications,” IS&T/SPIE Symposium Electronic Imaging: Science
and Technology on Multimedia Processing and Applications, vol. 6074, San Jose, California
USA, Jan. 15 ~ 19, 2006.
3.
Munjo Kim, Sanggil Kang, Munchurl Kim, and Jaegon Kim, "Target Advertisement Service
Using TV viewers' Profile Inference," 2005 Pacific-Rim Conference On Multimedia, Part I,
LNCS 3767, pp.202~211, Jeju, Korea, Nov. 13 ~ 16, 2005.
Conference Papers (Domestic)
1.
김문조, 이범식, 임정연, 김문철, 이희경, 이한규, "다단계 분류기의 사용자
프로파일 추론을 통한 프로토타입 표적 광고 시스템 개발,” 대한전자공학회
추계종합학술대회, 제28권, 제2호, pp. 991 ~ 994, 11월 26일, 서울대학교, 2005.
2.
김문조, 임정연, 강상길, 김문철, 강경옥, "시청자 프로화일 추론 기법을 이용한
표적 광고기술 연구," 한국방송공학회 학술대회, pp. 147 ~ 150, 11월 27일,
국민대학교, 2004.
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Patents (Domestic)
1.
이동성을 보장하는 다중 사용자 지원 멀티미디어 컨텐츠 제공 시스템과 그 제공
방법 {Multi-user support Content Mobility Method and Apparatus}, 출원번호: 10-20050071725, 2005.
2.
시청자의 TV 시청 데이터와 TV Anytime 메타데이터를 이용한 표적 광고 서비스
시스템 및 표적 광고 선별 방법 {Target Advertisement System and Method for
Selecting Target Advertisement Contents based on a TV Viewer’s TV Viewing History Data
and TV Anytime Metadata}, Patent Pending, 2005.
3.
디지털 방송 콘텐츠 및 콘텐츠 정보 데이터의 변환 방법 및 장치 {Adaptation
Method and Device of Digital Broadcasting Contents and Contents Information Data}, Patent
Pending, 2005.
Program Registration
1.
사용자 프로파일 추론 알고리즘 프로그램 {Program for User Profile Inference
Algorithm}, Registered in ETRI 2005.
Awards
1.
“다단계 분류기의 사용자 프로파일 추론을 통한 프로토타입 표적 광고 시스템
개발,” 우수논문상, 대한전자공학회 2005년 추계종합학술대회
79