Collaborative Filtering and Recommender Systems

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Collaborative Filtering and Recommender Systems

• Brian Lewis
• INF 385Q
• Knowledge Management Systems
• November 10, 2005

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Presentation Outline

• Collaborative filtering and recommender systems
  defined
• Novel example
• Readings - overview key concepts
• Glance, Arregui Dardenne (1997)
• Konstan, Miller, et al. (1997)
• Proctor McKinlay (1997)
• Conclusions
• References

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Collaborative Filtering defined

• "Based on the premise that people looking for
  information should be able to make use of what
  others have already found and evaluated." (Maltz
  Ehrlich, 1995)
• "Technique for dealing with overload in
  information environments" (Procter McKinlay,
  1997)

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Recommender systems defined

• Systems that evaluate quality based on the
  preferences of others with a similar point of view

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Hobo symbols from http//www.slackaction.com/signr
oll.htm
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Hobo symbols as RS?

• Specific to a community
• Implicit and explicit signs
• Filtered through encoding
• Cold-start problem?

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Compare to today

• Recommend
• Don't recommend

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Glance, Arregui Dardenne (1997)

• Knowledge Pump
• Designed for use with an electronic repository
• Document management and recommendation
• Community-centered collaborative filtering
• Characteristics
• Social filtering
• Content-based filtering

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Glance, Arregui Dardenne (1997)

• User-item matrix of ratings

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Konstan, Miller, et al. (1997)

• GroupLens
• Pilot study - Usenet news
• Rating system
• Integrate into an existing system/existing users
• Use existing applications - open architecture
• Characteristics
• High volume / high turnover
• High noise information resource
• Sparse set of ratings
• Predictive utility cost/benefit

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Konstan, Miller, et al. (1997)

• Predictive utility
• Risk - costs of misses andfalse positives
• Benefit - values of hits and correct rejections
• Usenet has high predictive utility
• High volume
• Value of correct rejection is high
• Risk of a miss is low

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Konstan, Miller, et al. (1997)

• Challenges
• Ratings sparsity
• "first-rater" problem
• Partition articles into clusters
• Capture implicit ratings
• Filter bots
• Performance challenges
• System architecture
• Composite users

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Proctor McKinlay (1997)

• Social Affordances and Implicit Ratings
• How implicit approaches might be improved
• Sources of rating and recommendation data
• Context of ratings and recommendations
• Real and virtual groups
• Privacy and accessibility

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Proctor McKinlay (1997)

• Characteristics
• Explicit ratings systems
• Reader ratings based approach is expensive
• How do you deal with trust issues?
• Implicit ratings systems
• Free to users
• How do you capture context?

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Proctor McKinlay (1997)

• Social Affordances
• "making the potential for social (inter)action
  visible."
• How can activities be made visible? (explicitly)
• Web bookmarks
• Sharable annotations
• How can activities be made visible? (implicitly)
• Copy browsing behavior of experts (virtual
  groups)
• Documents context in a group of documents
  (discourse analysis)
• Temporal coherence

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Proctor McKinlay (1997)

• Extracting implicit ratings from web behavior
• Virtual group proxies
• Proxy cache analysis
• Nominal rating
• Frequency
• Sequential accountability
• Distributional accountability
• Sources
• Topical coherence
• Temporal coherence
• Privacy Issues

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Conclusions

• Many different issues
• Diverse domains / communities
• Diverse content needs
• Context dependent
• Nature of information
• Predictive utility
• Very creative solutions to draw from

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References

• Glance, N., Arregui, D., Dardenne, M. (1997).
  Knowledge Pump Community-centered collaborative
  filtering. 5th DELOS workshop on filtering and
  collaborative filtering, Budapest, Hungary.
• Konstan, J., Miller, B., Maltz, D., Herlocker,
  J., Gordon, L. and Riedl, J. (1997), Applying
  collaborative filtering to usenet news,
  Communication of the ACM, 40(3), 77-87.
• Maltz, D. and Ehrlick, K. (1995). Pointing the
  way active collaborative filtering. CHI '95, ACM
  Press.
• Procter, R. and A. McKinley (1997). Social
  affordances and implicit ratings for social
  filtering on the Web. DELOS workshop on
  collaborative filtering, Budapest, Hungary.

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Questions

• Questions live here