“Experience with Personalization on Yahoo”
And
“Personalized Search”
Presenter: Rob Drum
Three Personalized Yahoos!
► My
Yahoo!
► Yahoo! Companion
► Inside Yahoo! Search
My Yahoo
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Customized personal copy of Yahoo
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Hundreds of choices for modules
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Updated automatically giving the user all the most current info.
Personalization
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As specific as TV channels shown in your guide and what local
provider you use to tell exact channels, or as general as top health
news
Layout can be personalized as well
Automatic personalization?
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i.e. news, stocks, weather, sports scores
Sports module can show teams found in users profile
Updates at specific times from 15 min to several hours
Easy to add easy to remove
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Modules can be selected from any yahoo site (technology news will
say next to it “add to my yahoo”). Can be removed or edited easily
from your personal home page
Yahoo! Companion
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Toolbar at the top by the browser
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Several choices of interfaces (currently two:
regular and stock market)
Customizable mini yahoo that allows users to
access yahoo content from anywhere on the web
Can be accessed from any computer because all
settings including bookmarks are stored on the
server
Inside Yahoo! Search
• Able to complement the usual search with
“direct focused content that can
sometimes be personalized”
– Example query “300”
• Returns pointer to “yahoo movies”
• Yahoo movies shows a movie image, cast and movies
show times
– If the user has been to this page before and entered
in a zip code then this will be remembered and
automatically used to update show times. Of course
zip code can be changed by user.
Large Scale
 Access
 Speed is of the utmost importance
 Custom built UDB
 Allows for hot backup at all times
 Optimized, cached, fully redundant communication between UDB and
MY Yahoo! Page display machines
 Privacy and security
 Encrypted sensitive data on machines with restricted access
 Both internal and external people devoted specifically to privacy and
security measures
 User interface
 Predictability is important
 Users must understand how system works so that they will be able
to experiment with personalization
 News
 Specific Vs. Broad
 Because I read a medical journal, articles about cancer are
displayed. This can be confusing and can raise privacy concerns
 “I live in 35244 my weather is sunny”
 This is obvious, easy, and always helpful
General Observations and Lessons
Learned
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Most users take what is given to them and never
customize. Why?
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A great deal of effort should go to the default page
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Default page is so good there is no need for additional effort
Customization tools are too difficult
Many people do not need complex personalization
Zip codes are crucial on default page. They give great personal
news based on geographic location
Power users will do amazing things—never
underestimate them
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Design everything for infinite growth, do not use artificial limits
unless absolutely necessary
Some users have sites as big as 500KB
General Observations and
Lessons Learned Cont.
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People generally don’t understand the
concept of personalization
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Such personalization did not previously exist
We must do everything possible to make sure it
is presented in an intuitive and useful way
Make sure you address all your users
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Site must be able to adapt
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Users outside U.S. must be able to use the
features in the same way
Users on other platforms (variety of browsers,
linux, mac )
Learn from users
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Must constantly look at logs to see user activity
It is often surprising- Users will always find an
unexpected way to use any new technology
Conclusion to Yahoo! Personalization
“The challenge is to combine business,
technology, and society issues in a way
that benefits consumers without violating
their privacy.”
Power users get the most out of current
customization
The entry bar must be lowered to allow lower
level users to experience the same benefits it
must be made clear that personalization is
possible
Personalized Search
• Contextual Computing- refers to the enhancement
of a users interactions by understanding the user,
the context, and the applications of the information
being used, typically across a wide set of user
goals. Contextual computing is not just about
modeling user preferences and behavior or
embedding computation everywhere, it’s about
actively adapting the computational environment—
for each and every user—at each and every point of
computation
• Contextual computing approach to search
•
move from consensus relevancy to personal specific
relevancy
• This will decrease the time that it takes both novices and
experts to find information
Information Retrevial
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Currently Google techniques
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Author relevancy
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Problem entire population does not value terms in the same
way as certain authors or authoring community
Usage based
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Hyperlinks and Citation
Frequency, recently and or duration of interaction by users
Computes relevancy for entire community
Forgets to take into account that relevance is relative for each
user
What is needed is a way to compute PERSONAL
RELEVANCY
The Outride Approach
Contextualization- interrelated conditions that
occur within an activity
Includes- nature of information, the applications in use,
when, and so on
Individualization- totality of characteristics that
distinguishes and individual
User’s goals, prior and tacit knowledge, past informationseeking behaviors, among others
Careful integration into user interface
What the system is doing to personalize as well as to
undo personalization
Personalize search for an active searcher
Query augmentation
Result processing
The Outride Approach Cont.
• “Personalization engine sits between a user interface
and an intra/Internet search engine. Once a user has
entered a query, the query can be compared against the
contextual information available to determine if the query
can be refined to include other terms”
– EX: user is looking at a series of pages on car information and
queries “contour” the system may augment the query by adding
the term “car” or “ford” to provide the user with results about the
Ford Contour car.
– EX: if search is on a topic previously seen, the system can
reinforce query with similar terms of suggest results from prior
searches.
– Will not augment if it is a new topic
– Final result of a query augmentation is a more precise query that
will be submitted to a search engine for processing
Outride Approach Cont.
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Results can be individualized based on information in the users model
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Demographic information
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Also re-rank search results based on similarity of content and the user’s
profile
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Allows users to identify the most popular, faddish and time-consuming pages they
have visited
Divided into “have seen, haven’t seen”
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Java programmer gets info related to developers
Teacher of programming languages gets info about Java tutorials and overviews
Re-ranking results based on frequency, how long ago, or duration of usage
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Prevent minors from seeing adult content
Show me only what I know
Show me only what I don’t know
Searches can be aggregated across multiple profiles
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Most popular among hikers
Most popular among entire population
Outride Personalized Search
System
 Generalized architecture for the personalization of
search across a variety of information ecologies
 As a part of the browser the component supports
direct manipulation and has access to all user
interactions
 Four separate info spaces
 Personal hierarch of user’s links (personal)
 Catalog of links (directory)
 User’s surf history (history)
 Search results from the entire web (web)
 Browse or search under each tab
 Effectively defines the scope of activities available for
search
Open Directory Project (ODP)
• Each users has own weighting across top
1,000 categories of the ODP
• If user starts with favorites they are
classified and this is the base of the ODP
• If no content is stored in the beginning
then the sidebar will store the next 1,000
clicks and keep this database updated
Query Augmentation
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Query augmentation is preformed by
integrating various clues provided by the
instrumentation of the interface and user
models
 Comparing
the query to the users’ content
profile using vector methods provide better
results
Result Set Processing
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Performed across typically 1,000 results
Filtered by “have seen, have not seen”
Usage based re-rankings
Re-ranking by user model profile
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Titles and other metadata are compared via vector methods
against the user profile
“We found the combination of query augmentation
and result processing with a contextually designed
interface to be quite effective in making search easier
and faster”
Testing Methodology and Results
• Tested using metrics (number of actions taken)
• Tested by independent company
• Measured time and action until finding sufficient
•
information
Query topics
– Football
– Car shopping
– Travel planning
• Default user model was used for all participants
Results
Issues
 Some tasks were directly supported by
the functionality provided by the Outride
system
 Use of default profile
 User interface for Outride is constantly
seen
Future Directions
• Modeling a user’s changing interests over time
• How long ago and frequency helps, but there will
always be exceptions
• Carefully designed interfaces will help
• Success of query augmentation rests on detecting
user context switches
• Incorrect augmentation can be fixed by users
deciding to turn off personalization
• Privacy
• All users can see their profiles and histories as well
as ODP interest weights
• Users control what is used by turning it on and off
• Result sharing is no longer possible
• Can also be addressed at the interface level
Conclusion to Outride
 “We have presented here a new type of IR
system that personalizes the search
experience for each user across their
interactions. We have shown initial
evidence to support our firm conviction that
the contextualized computing approach
toward the personalization of search is the
next frontier toward significantly increasing
search efficiency”