“Personalized Search”
By James Pitkow
..a contextual computing approach
may prove a breakthrough in
personalized search efficiency..
Ashley Twichell
Emily Lang
Contextual Computing
• Refers to the enhancement of a user’s interactions by understanding the
user, the context, and the applications and information being used
• It’s about “actively adapting the computational environment - for each
and every user - at each point of computation”
• “Focuses on understanding the information consumption patterns of
each user, the various information foraging strategies and applications
they employ, and the nature of the information itself”
• A shift from “consensus relevancy” (relevancy for entire population
used for every person) to “personal relevancy” (relevancy is determined
for each individual)
• This shift to personal relevancy decreases the time it takes people to
find information
Review of Information Retrieval
• Content-based approaches - using language to match a query with results - this
approach doesn’t help users determine which results are actually worth reading
• Author-relevancy techniques - using citation and hyperlinks - sometimes
presents the problem of ‘authoring bias’ and/or ‘ranking bias’ (results that are
valued by authors are not necessarily those valued by the entire population)
• Usage rank - this “leverages the actions of users to compute relevancy” - the
usage rank is computed from the frequency, recency, and/or duration of
interaction by users - usage ranks allow for changes in relevancy over time to be
determined
• All of the above techniques measure relevance “as a function of the entire
population of users”
• This does not acknowledge that “relevance is relative” for each user
• There needs to be a way to “take into account that different people find
different things relevant and that people’s interests and knowledge change over
time - “personal relevance”
The Outride Approach
• In order to personalize search, we need to combine at least two
different computational techniques - contextualization and
individualization
• Contextualization - “the interrelated conditions that occur within
an activity..includes factors like the nature of information
available, the information currently being examined, and the
applications in use”
• Individualization - “the totality of characteristics that distinguishes
an individual.. Uses the user’s goals, prior and tacit knowledge,
past information-seeking behaviors”
• Main ways to personalize a search are “query augmentation” and “result
processing”
• Query augmentation - when a user enters a query, the query can be
compared against the contextual information available to determine if
the query can be refined to include other terms
• Query augmentation can also be done by computing the similarity
between the query term and the user model - if the query is on a topic
the user has previously seen, the system can reinforce the query with
similar terms
• This more concise query is then shown to the user and “submitted to a
search engine for processing”
• Once the query has been augmented and processed by the search
engine, the results can be “individualized”
• The results being individualized - this means that the information is
filtered based upon information in the user’s model and/or context
• The user model “can re-rank search results based upon the similarity of
the content of the pages in the results and the user’s profile”
• Another processing method is to re-rank the results
based upon the “frequency, recency, or duration of
usage..providing users with the ability to identify the
most popular, faddish and time-consuming pages
they’ve seen”
• “Have Seen, Have Not Seen” - this features allows
new information to be identified and return to
information already seen”
The Outride Personalized Search
System
• Designed to be a “generalized architecture for the personalization
of search across a variety of information ecologies”
• The Outride client can be integrated into the sidebar of the
Internet - it “supports direct manipulation and has access to all
user interactions”
• Sidebar is split up into four separate information spaces Personal (personal hierarchy of each user’s links), Directory (a
catalog of links), History (user’s surf history), Web (search results
from the entire Web)
• The user models are computed from the content in these
information spaces in the sidebar
Testing Methodology and Results
• Outride used eTesting Lbs to design a series of test to measure if the
Outride system actually succeeded in making searches faster and easier
to complete
• The elapsed time to successfully complete a search and the number of
interface actions (mouse clicks/number of entries entered) were used as
the measurements
• Participants performed 12 search tasks with Outride and a different
search engine
• Default user model was used for all participants
• Participants found the answers more quickly with Outride than with any
other search engine - on average, participants took 39 seconds to
complete the tasks using Outride and 75 seconds using Google
• Participants also needed fewer interface actions when using Outride - 11
when using Outride and 21 using the other search engine
Issues/problems with the experiments
• Some of the scenarios contained tasks “directly supported
by the functionality provided by the Outride system,
creating an advantage against the other search engines”
• Default profiles were used, instead of individualized
profiles - therefore, it did not “represent the test
participant’ actual surfing patterns, nor were the
participants intimately familiar with the content of the
profiles”
• Despite these issues, the “magnitude of the difference
between the Outride system and the other engines is
compelling”
Future Directions
• One problem is modeling a user’s changing interests
over time
• However, carefully designed interfaces can help
“alleviate inaccurate personalization and allow users to
control the extent of the personalization”
• Privacy issues are a problem since it is a system that
stores models based upon user’s interactions with
information
“Experience with Personalization
on Yahoo!
“When designing Web
personalization products, make sure
you address all your users”
By Udi Manber, Ash Patel, and John
Robison
Overview
• This article discusses three different
examples of personalization on Yahoo!
Including
– My Yahoo!
– Yahoo! Companion
– Inside Yahoo! Search
My Yahoo!
• My Yahoo! Is a customized personal copy of
Yahoo!
• Users select from various models such as
news, stock prices, weather, and sports
scores to put on their Web page.
• Provides users with the latest information on
every subject, but with only the specific items
they want to know about.
My Yahoo! Features
•
•
•
•
Personalization
– Users can do such things as chose certain TV channels to put in their TV Guide
Customized Content
– Example of this is a sports module that lists the teams in the user’s area after obtaining
that information from the user’s profile.
Automatic Updates
– A My Yahoo! Option allows this page to automatically update at any user-specified
interval from 15 minutes to several hours
Original Module Ability
– Modules can be selected from a long list, but can also be added by clicking on a
button at the original content page.
– Each module on a My Yahoo! Page also has an edit and remove button, allowing
users to manipulate their pages directly, without ever needing to visit an edit/layout
page.
Yahoo! Companion
• A browser’s embedded toolbar from which a
user can directly access most of Yahoo!
features from anywhere on the Web.
• Like a mini My Yahoo! that takes a small
space at the top of the page is always with
you.
Yahoo! Companion Features
• The user interface is similar to any other bookmark
feature, but the difference is the bookmarks are
kept on the server (not simply on the specific
computer)
• Therefore changes that users make to their toolbar
will stay with users even if they switch to a different
computer
• Users have the ability to chose from several
toolbars (such as a regular one a stock market one)
and change them at any time
Inside Yahoo!
• Yahoo! like many other search engines tries
to personalize searches using information it
is able to obtain from the user
• It would be impossible for Yahoo! to
customize every search.
Yahoo! Search Example
• If a user searches for the name of current movie, Yahoo will
show results for Yahoo! Movies, show an image for the
movie, the cast, and a pointer to a page with current show
times
• If the user had looked at showtimes on a page previously
and entered a zip code, Yahoo! can now use that
information to show the user movie times in his or her own
are
Solving Privacy Problems
• Any company that collects private information must guard that
information with its life.
• Personal information about Yahoo! Is maintained in a specially designed
User Database (UDB) which was built on Yahoo!’s own customized
software.
• Yahoo! has data replication and distribution capabilities allowing them to
replicate and distribute the UDB over secure links to remote locations in
Asia and Europe
• Yahoo! has enlisted a security-audit company to evaluate our procedures
periodically and suggest necessary changes, as well as employ several
internal people devoted solely to privacy and security issues.
User Interface
• The issue of usability focuses mostly on the issue of predictability
• Personalization features that learn what user want and attempt to satisfy them
are hotly debated
• A weakness in these personalization features is unpredictability
• Example: A lot of people do not want customized news, they want just the
same news as everyone else
• Also getting news about cancer because a user some medical journal on
cancer in the past can confuse the user and even jeopardize user trust and
raise serious privacy concerns in the user’s mind
• Any effective personalization feature should encourage experimentation.
Observations/Lessons Learned
• Most users take what is given to them and never customize.
• Even though companies like Yahoo! offer customized pages
for users, a great deal of effort must still go into the default
page.
• Companies should never underestimate power users
• Customization should follow you as much as possible
• People generally don’t understand the concept of
customization
• Make sure you address all your users
• Learn from users
Conclusion
• “Too many attempts have been made without sufficient
regard to what people really want, what they can use, and
how best it should fit their needs.”
• “A major challenge to large-scale personalization is to lower
the entry bar, making it easier for less-experienced users to
customize their pages, and making it clear to novices that
customization is possible.”