Collaborative Filtering Shaun Kaasten CPSC 601.13 CSCW.

Collaborative Filtering

Shaun Kaasten

CPSC 601.13 CSCW

Outline

What is filtering?Filtering techniquesWhy should we use CF?Examples of CF systemsVirtual communityCF design goalsEvaluation formsActive CFSummary

What is Filtering?

Information overload

Finding desired information

Eliminating undesirable

’94 Resnick et al.

Filtering Techniques (in and out)

Cognitive (content) Text in the item

Economic Costs and benefits

Mass mailings (low production costs)

Social People and judgments

Collaborative filtering – subjective evaluations of others

’87 Malone et al.

Why should we use CF?

People are better at subjective evaluationsWriting style,clarity, music, cake recipes

Benefit from seeing the history of an object’s useRead/edit wear

’95 Maltz & Ehrlich

Tapestry (1992)

Xerox PARC

Users annotate documents they read

Helped others decide what to read

FailuresNot freeNot distributedSQL interface – difficult to browse

Grouplens (1994)

Bellcore Video CF (1994)

Suggested Videos for: John A. Jamus. Your must-see list with predicted ratings:

•7.0 "Alien (1979)" •6.5 "Blade Runner" •6.2 "Close Encounters Of The Third Kind (1977)"

Your video categories with average ratings: •6.7 "Action/Adventure" •6.5 "Science Fiction/Fantasy" •6.3 "Children/Family"

The viewing patterns of 243 viewers were consulted. Patterns of 7 viewers were found to be most similar. Correlation with target viewer:

•0.59 viewer-130 ([email protected]) •0.55 bullert,jane r ([email protected])

Bellcore Community Web Browser (1995)

http://www.apparent-wind.com/navigation/images/enhanced-mosaic.gif

Movielens (1998?)

Web CF: Amazon Customer Reviews

Web CF: Cnet User Opinions

Web CF: MSDN Article Ratings

Virtual Community

’95 Hill et al.

Influence each other without interacting

Share benefits of collaboration without costsTime – developing personal relationshipsPrivacySynchronous communication

No intelligent agents (other than people)

CF Design Goals: Bellcore & Grouplens

Common Easy participation People power, not agents Prediction accuracy increases with user base size

Grouplens Compatibility Privacy Rich recommendations

Bellcore Works for groups, not just individuals Recommendations should include confidence

Evaluation Forms

ExplicitMusic reviews on AmazonGrouplens- grading of Usenet message

ImplicitGrouplens – monitor how long a user reads

an article

History-Enriched Digital Objects

’94 Hill et al.

Trade off: Effort vs. Rewards

’95 Hill et al.

Finding Similar Tastes

Compute correlation coefficients for the user’s reviews and others

Use as weights to combine the ratings for current article

Correlation avoids differences of scale interpretation

’94 Resnick et al.

Cold Start Problem

Profile needed to find similar tastesTraining periodNo immediate benefit for user (Grudin’s

rule)

Restricted from new areas


Active CFPassive No direct connection between evaluator & reader Works for: many documents in a single database

Active Intent to share knowledge with particular people Works for: distributed systems, where just finding

sources is difficult Benefit increases with the divergence of the

documents


Case Study: Computer Support Center

Expectation: workers use on-line or printed documentation to answer problems

Finding: rely on each other

Information mediator Skilled at finding and applying info


Build a system to support…

Collaboration and information sharing amongst colleagues

Information mediators sending out references and commentary of useful documents


What informal methods are missing

Contextual information Name, source, date, sender information

Ease of use Add annotations Return benefits early - no cold start

Flexibility Method of distribution, comments and context No set roles


The Pointer System

Distribution of Pointers

Private database – bookmarks

Email Individuals Subscribe-only mailing lists

Information digests Pre-designed document – newsletters, reports,

etc.


Challenging Common Theories

Comment providers should be anonymous

Knowing something about commenter is critical to evaluating the usefulness of that document


Challenging Common Theories

Information finders should be freed from addressing and sending mail

Users really do have recipients in mind when they discover information

Irony of Active CF

Recipients are passiveCannot use system to find reviewed

information


Summary

Choice under uncertaintyBenefit from knowledgeable people

Virtual community of experts (?)

Active CF systems help point colleagues to informationPassive CF help ‘explorers’ learn from the community

Collaborative Filtering Shaun Kaasten CPSC 601.13 CSCW.

Documents

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