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Collaborative Filtering

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Collaborative Filtering Shaun Kaasten CPSC 601.13 CSCW – PowerPoint PPT presentation

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Title: Collaborative Filtering


1
Collaborative Filtering
  • Shaun Kaasten
  • CPSC 601.13 CSCW

2
Outline
  • What is filtering?
  • Filtering techniques
  • Why should we use CF?
  • Examples of CF systems
  • Virtual community
  • CF design goals
  • Evaluation forms
  • Active CF
  • Summary

3
What is Filtering?
  • Information overload
  • Finding desired information
  • Eliminating undesirable

94 Resnick et al.
4
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.
5
Why should we use CF?
  • People are better at subjective evaluations
  • Writing style,clarity, music, cake recipes
  • Benefit from seeing the history of an objects
    use
  • Read/edit wear

95 Maltz Ehrlich
6
Tapestry (1992)
  • Xerox PARC
  • Users annotate documents they read
  • Helped others decide what to read
  • Failures
  • Not free
  • Not distributed
  • SQL interface difficult to browse

7
Grouplens (1994)
8
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 (unlisted_at_merl.com)
  • 0.55 bullert,jane r (bullert_at_cc.bellcore.com)

9
Bellcore Community Web Browser (1995)
10
Movielens (1998?)
11
Web CF Amazon Customer Reviews
12
Web CF Cnet User Opinions
13
Web CF MSDN Article Ratings
14
Virtual Community
  • Influence each other without interacting
  • Share benefits of collaboration without costs
  • Time developing personal relationships
  • Privacy
  • Synchronous communication
  • No intelligent agents (other than people)

95 Hill et al.
15
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

16
Evaluation Forms
  • Explicit
  • Music reviews on Amazon
  • Grouplens- grading of Usenet message
  • Implicit
  • Grouplens monitor how long a user reads an
    article

17
History-Enriched Digital Objects
94 Hill et al.
18
Trade off Effort vs. Rewards
95 Hill et al.
19
Finding Similar Tastes
  • Compute correlation coefficients for the users
    reviews and others
  • Use as weights to combine the ratings for current
    article
  • Correlation avoids differences of scale
    interpretation

94 Resnick et al.
20
Cold Start Problem
  • Profile needed to find similar tastes
  • Training period
  • No immediate benefit for user (Grudins rule)
  • Restricted from new areas

95 Maltz Ehrlich
21
Active CF
  • Passive
  • 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

95 Maltz Ehrlich
22
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

95 Maltz Ehrlich
23
Build a system to support
  • Collaboration and information sharing amongst
    colleagues
  • Information mediators sending out references and
    commentary of useful documents

95 Maltz Ehrlich
24
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

95 Maltz Ehrlich
25
The Pointer System
26
Distribution of Pointers
  • Private database bookmarks
  • Email
  • Individuals
  • Subscribe-only mailing lists
  • Information digests
  • Pre-designed document newsletters, reports,
    etc.

95 Maltz Ehrlich
27
Challenging Common Theories
  • Comment providers should be anonymous
  • Knowing something about commenter is critical to
    evaluating the usefulness of that document

95 Maltz Ehrlich
28
Challenging Common Theories
  • Information finders should be freed from
    addressing and sending mail
  • Users really do have recipients in mind when they
    discover information

29
Irony of Active CF
  • Recipients are passive
  • Cannot use system to find reviewed information

95 Maltz Ehrlich
30
Summary
  • Choice under uncertainty
  • Benefit from knowledgeable people
  • Virtual community of experts (?)
  • Active CF systems help point colleagues to
    information
  • Passive CF help explorers learn from the
    community
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