International Journal of Engineering Trends and Technology (IJETT) – Volume 4 Issue 10 - Oct 2013
Personalized Query Based Search Techniques Using Association
Rules
P.Karuppusamy1, Mrs. T. Lakshmipriya2
M.Phil Scholar, Government Arts College (Autonomous), Coimbatore.
2
M.Phil Scholar, Kaamadhenu Arts and Science College, Sathiyamangalam
1
ABSTRACT
In mobile based search major problem is
that interaction between the user and search are
controlled by little numeral of factors in the
mobile plans. By observing of necessitate for
dissimilar
types
of
concepts,
present
personalized mobile search engine (PMSE), it
capture the user preferences concepts by mining
click through data. In PMSE the user
preferences are ordered in an ontology-based,
user profile to adapt a personalized ranking
function for future search results. In proposed
system introduce an association rule mining
algorithm to collect the travel related query
patterns and travel patterns from the original
personal mobile search engine profile.
Association rule learning is a popular and well
researched method for discovering interesting
relations between variables in large databases.
They introduced association rules for
discovering regularities between normal patterns
and query related patterns in the personalized
mobile search engine result.
Data mining software is one of a
number of analytical tools for analyzing data. It
allows users to analyze data from many different
dimensions or angles, categorize it, and
summarize the relationships identified.
Technically, data mining is the process
of finding correlations or patterns among dozens
of fields in large relational databases.
Data
Data are any facts, numbers, or text that
can be processed by a computer. Today,
organizations are accumulating vast and growing
amounts of data [2] in different formats and
different databases. This includes:
Keywords: Mobile Search Engine, Association
rule, Travel Pattern.
1. INTRODOCTION

Operational or transactional data
such as, sales, cost, inventory,
payroll, and accounting

Nonoperational data, such as
industry sales, forecast data, and
macro economic data

Meta data - data about the data
itself, such as logical database
design or data dictionary definitions.
1.1 Data Mining
Data mining is the process of analyzing
data
from
different
perspectives
and
summarizing it into useful information.
Data mining involves six common classes of
tasks
ISSN: 2231-5381
http://www.ijettjournal.org
Page 4411
International Journal of Engineering Trends and Technology (IJETT) – Volume 4 Issue 10 - Oct 2013

The identification of unusual data
records that might be interesting or data
errors and require further investigation.

Association rule learning Searches for
relationships between variables. This is
Figure1.Data Mining Process
sometimes referred to as market basket
analysis.

Clustering is the task of discovering
groups and structures in the data [5] that
In the problem definition phase, data mining
tools are not yet required.
Data Exploration
are in some way or another "similar",
without using known structures in the
data.

Classification is the task of generalizing
known structure to apply to new data.

Regression attempts to find a function
which models the data with the least
Domain experts understand the meaning
of the metadata. They collect, describe, and
explore the data. They also identify quality
problems of the data. A frequent exchange with
the data mining experts and the business experts
from the problem definition phase is vital.
Data Preparation
error.

Summarization
providing
a
more
compact representation of the data set,
including
visualization
and
report
Domain experts build the data model for
the modelling process. They collect, cleanse, and
format the data because some of the mining
functions accept data only in a certain format.
generation.
Modelling
1.2 Data Mining Process
Problem Definition
A data mining project starts with the
understanding of the business problem. Data
mining experts, business experts, and domain
experts work closely together to define the
project objectives and the requirements. The
project objective is then translated into a data
mining problem definition.
ISSN: 2231-5381
Data mining experts select and apply
various mining functions because you can use
different mining functions for the same type of
data mining problem. Some of the mining
functions require specific data types. The data
mining experts must assess each model.
http://www.ijettjournal.org
Page 4412
International Journal of Engineering Trends and Technology (IJETT) – Volume 4 Issue 10 - Oct 2013
Evaluation
1.4 Motivation of Research
Data mining experts evaluate the model.
If the model does not satisfy their expectations,
they go back to the modelling phase and rebuild
the model by changing its parameters until
optimal values are achieved.
Capturing a user’s interests for
personalization is to analyze the user’s click
through data. Leung et al. developed a search
engine personalization method based on users’
concept preferences and showed that it is more
effective than methods that are based on page
preferences. However, most of the previous
work assumed that all concepts are of the same
type. Major motivation of the research is to
improve personalization results and finds the
most important query based results to satisfy the
user preference profiles based [9] on concepts
and locations. From the query travel patterns
privacy is also important facilitate smooth
control of privacy exposure while maintaining
good ranking quality.
Deployment
Data mining experts use the mining
results by exporting the results into database
tables or into other applications, for example,
spreadsheets.
1.3 Objective of Research
GPS locations play an important role in
mobile web search. For example, if the user,
who is searching for hotel information, is
currently located in “Shinjuku, Tokyo,” his/her
position can be used to personalize the search
results to favor information about nearby hotels.
Here, we can see that the GPS locations (i.e.,
“Shinjuku, Tokyo”) help reinforcing the user’s
location preferences (i.e., “Japan”) derived from
a user’s search activities to provide the most
relevant results.
Objective of the research is to
investigate methods to exploit regular travel
patterns and query patterns [13] from the GPS
and click through data to further enhance the
personalization effectiveness of PMSE. GPS
location also find the query based travel patterns
to find the most important click through files
travel patterns with frequent itemset that
frequent user patterns from the search based
results. It is capable of combining a user’s GPS
locations and location preferences into the
personalization process.
ISSN: 2231-5381
1.5 Problem Statement
A major problem in mobile search is
that the interactions between the users and
search engines [10] are limited by the small
form factors of the mobile devices. As a result,
mobile users tend to submit shorter, hence, more
ambiguous queries compared to their web search
counterparts. In order to return highly relevant
results to the users, mobile search engines must
be able to profile the users’ interests and
personalize the search results according to the
users’ profiles.
2. RELATEDWORK
2.1 Association Rule Mining
Association rule learning is a popular
and well researched method for discovering
interesting relations between variables in large
databases. It is intended to identify strong rules
discovered in databases using different measures
of interestingness. Association rules are usually
required to satisfy a user-specified minimum
support and a user-specified minimum
confidence at the same time.
http://www.ijettjournal.org
Page 4413
International Journal of Engineering Trends and Technology (IJETT) – Volume 4 Issue 10 - Oct 2013
Association rule generation is usually split up
into two separate steps:
1. First, minimum support is applied to find all
frequent itemsets in a user clickthrough data.
2. Second, these frequent itemsets and the
minimum confidence constraint are used to
form rules
To find a query traveler’s interest
extracted from search based user click through
files when the personal user search the results
from mobile .When user enter query based path
or traversal [15] patterns are identified firstly
and then we generate frequent itemset that is
number of time the user click thorough files and
find most important travel patterns in the click
through files. This research focuses on the
travelers who use mobile search have most
frequent based links in both location and concept
based ontology ,before that we find the frequent
itemset that is more number of times user search
the similar web pages or concept and location
Proposed ARM based query travel [7] pattern
to search for travel destination that is user
concept results ,applied data mining method
and association rules technique to analyze
the relationship between travelers’ profile and
their transactions in the data. From this we
analyze the most important pattern to search the
results and can increase opportunity for the
competitive operations of tourism firm to
respond the travelers demand effectively.
2.2 PMSE Clients for User Click through
In the PMSE’s client-server architecture,
PMSE clients are responsible for storing the user
click through and the ontologies [6] derived
from the PMSE server. Simple tasks, such as
updating click though and ontologies, creating
feature vectors, and displaying re-ranked search
results are handled by the PMSE clients with
limited computational power.
ISSN: 2231-5381
The user profiles for specific users are
stored on the PMSE clients, thus preserving
privacy to the user PMSE has been prototyped
with PMSE clients on the Google Android
platform.
2.3 User Interest Profiling
Recognize that the same content or location
concept may have different degrees of
importance to different users and different
queries. To formally characterize the diversity of
the concepts associated [3] with a query and
their relevances to the user’s need, we introduce
the notion of content and location entropies to
measure the amount of content and location
information associated with a query. Similarly,
to measure how much the user is interested in
the content and/or location information in the
results, we propose click content and location
entropies. Based on these entropies, we develop
a method to estimate the personalization
effectiveness [4] for a particular query of a given
user, which is then used to strike a balanced
combination between the content and location
preferences. The results are re-ranked according
to the user’s content and location preferences
before returning to the client. Personalized
mobile search engine is an innovative approach
for personalizing web search results.
2.4 Location ontology
Our approach for extracting location
concepts is different from that for extracting
content concepts. We observe two important
issues in location ontology formulation. First, a
document usually embodies only a few location
concepts, and thus only very few of them cooccur with the query terms [12] in web-snippets.
To alleviate this problem, we extract location
concepts from the full documents.
http://www.ijettjournal.org
Page 4414
International Journal of Engineering Trends and Technology (IJETT) – Volume 4 Issue 10 - Oct 2013
Second, the similarity and parent-child
relationship cannot be accurately derived
statistically because the limited number of
location concepts embodied in documents.
Furthermore, many geographical relationships
among locations have already been captured as
facts.
•
system
has
the
following
The PMSE doesn’t exploit regular travel
patterns and query patterns from the
GPS.
3.2 Proposed System
3. METHODOLOGY
3.1 Existing System
In existing systems to mine document
preferences from click through data. It is to
combine a spying technique together with a
novel voting procedure to determine user
preferences. It introduced an effective approach
to predict users’ conceptual preferences from
click through data for personalized query
suggestions.
PMSE
PMSE by adopting the meta search
approach which relies on one of the commercial
search engines, such as Google, Yahoo, or Bing,
to perform an actual search. The client is
responsible for receiving the user’s requests,
submitting the requests to the PMSE server,
displaying the returned results, and collecting
his/her click through in order to derive his/her
personal preferences. The PMSE server, on the
other hand, is responsible for handling heavy
tasks such as forwarding the requests to a
commercial search engine, as well as training
and re-ranking of search results before they are
returned to the client. PMSE uses “concepts” to
model the interests and preferences of a user.
Since location information is important in
mobile search, the concepts are further classified
into two different types, namely, content
concepts and location concepts. PMSE addresses
this issue by controlling the amount of
information in the client’s user profile.
ISSN: 2231-5381
The existing
drawbacks:
Investigate methods to exploit regular
travel patterns and query patterns from the GPS
and click through data to further enhance the
personalization effectiveness of PMSE. For this
we apply the association rule mining based
system to mine the regular pattern with user and
location. Association rule learning is a popular
and well researched method for discovering
interesting relations between variables in large
databases. In this proposed association rule
mining analysis the query related patterns of the
user to identify strong rules discovered in
databases using different measures of
interestingness.
First is Support: It is simply the number
of transactions that include all items in the
antecedent and consequent parts of the rule that
is the number of patterns in the user click
through data with travel patterns.
Second is Confidence: It is the ratio of
the number of transactions that include all items
in the consequent as well as the antecedent to the
number of transactions that include all items in
the antecedent.
The proposed systems have the following
advantages:
•
Click through data to further enhance
the personalization effectiveness of
PMSE.
•
Improve personalization result with best
association result for each regular
pattern.
http://www.ijettjournal.org
Page 4415
International Journal of Engineering Trends and Technology (IJETT) – Volume 4 Issue 10 - Oct 2013
4. CONCLUSION
In existing system PMSE extract the
user preferences based content and location
based on the user click through data .To
acclimatize to the user mobility, it also
integrated the user’s GPS locations in the
personalization procedure to observe the
location and help to develop retrieval
effectiveness, particularly for location queries.
Proposed query travel patterns system
contributes new knowledge which gathers
increasingly on database to satisfy the user
profiles results new user is searching for travel
information on mobile devices, the system will
learn user behavior transaction which user
clicks. The system will collect new data and
analyze them then interpret to user. The
system will learn increasingly when several
users click more on mobile application. It will
collect more data and repeatedly analyze the
newly obtained data.
In addition, with the huge development
of the information presented on the Web, it is
very complicated for Web search engines to
satisfy the user information requirement only
with a short ambiguous query. Different Query
based results different from each user, query
based recommendation system will help user to
find the ambiguous query .In future work we
include the query based recommendation
process to identify the user similar queries and
their results.
5. REFERENCES
1. B. Smyth, “A Community-Based Approach to
Personalizing Web Search,” IEEE Journals,
Computer, Vol. 40, No. 8, Pp. 42 – 50, 2007.
ISSN: 2231-5381
2. B. Arzanian, F. Akhlaghian and P. Moradi,
“A Multi- Agent Based Personalized MetaSearch Engine Using Automatic Fuzzy Concept
Networks,” Third International Conference on
Knowledge Discovery and Data Mining, Pp. 208
– 211, 2010.
3. Chen Ding, J.C. Patra And Fu Cheng Peng,
“Personalized Web Search With Self-Organizing
Map,” The 2005 IEEE International Conference
On E- Technology, E-Commerce And E-Service
(EEE '05), Pp. 144 – 147, 2005.
4. C. Biancalana And A. Micarelli, “Social
Tagging In Query Expansion: A New Way For
Personalized Web Search,” International
Conference On Computational Science And
Engineering (CSE '09), Vol. 4, Pp. 1060 1065,
2009.
5. Dik Lun Lee, W. Ng and K.W.-T. Leung,
“Personalized Concept-Based Clustering of
Search Engine Queries,” IEEE Transactions on
Knowledge and Data Engineering, Vol. 20, No.
11, Pp. 1505 – 1518, 2008. 20.
6. F. Akhlaghian, B. Arzanian and P. Moradi,
“A Personalized Search Engine Using OntologyBased Fuzzy Concept Networks,” International
Conference on Data Storage and Data
Engineering (DSDE), Pp. 137 – 141, 2010.
7. Fang Liu, C. Yu And Weiyi Meng,
“Personalized Web Search For Improving
Retrieval Effectiveness,” IEEE Transactions On
Knowledge And Data Engineering, Vol. 16, No.
1, Pp. 28 – 40, 2004.
8. Han-joon Kim, Sungjick Lee, Byungjeong
Lee and Sooyong Kang, “Building Concept
Network-Based User Profile for Personalized
Web Search,” 9th International Conference on
Computer and Information Science (ICIS), Pp.
567 – 572, 2010.
http://www.ijettjournal.org
Page 4416
International Journal of Engineering Trends and Technology (IJETT) – Volume 4 Issue 10 - Oct 2013
9. J. Lai And B. Soh, “Personalized Web Search
Results With Profile Comparisons,” Third
International Conference On Information
Technology And Applications (ICITA 2005),
Vol. 1, Pp. 573 – 576, 2005.
13. O. Shafiq, R. Alhajj and J.G. Rokne,
“Community Aware Personalized Web Search,”
International Conference on Advances in Social
Networks Analysis and Mining (ASONAM), Pp.
351 – 355, 2010.
10. Kyung-Joong Kim And Sung-Bae Cho, “A
Personalized Web Search Engine Using Fuzzy
Concept Network With Link Structure,” Joint
9th IFSA World Congress And 20th NAFIPS
International Conference, Vol. 1, Pp. 81 – 86,
2001.
14. Wen-Chih Peng and Yu-Chin Lin, “Ranking
Web Search Results from Personalized
Perspective,” The 8th IEEE International
Conference on and Enterprise Computing, ECommerce, and E-Services, The 3rd IEEE
International Conference on E-Commerce
Technology, Pp. 12, 2006.
11. K.W.-T. Leung, D.L. Lee And Wang-Chien
Lee, “Personalized Web Search With Location
Preferences,”
IEEE
26th
International
Conference On Data Engineering (ICDE), Pp.
701 – 712, 2010.
12. M. Kutub, R. Prachetaa and M. Bedekar,
“User Web Search Behaviour,” 3rd International
Conference on Emerging Trends in Engineering
and Technology (ICETET), Pp. 549 – 554, 2010.
ISSN: 2231-5381
15. Xuwei Pan, Zhengcheng Wang And Xinjian
Gu, “Context-Based Adaptive Personalized Web
Search For Improving Information Retrieval
Effectiveness,” International Conference On
Wireless Communications, Networking And
Mobile Computing, Pp. 5427 – 5430, 2007.
http://www.ijettjournal.org
Page 4417