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Collaborative Filtering and Recommender Systems

Collaborative Filtering and Recommender Systems. Brian Lewis INF 385Q Knowledge Management Systems November 10, 2005. Presentation Outline. Collaborative filtering and recommender systems defined Novel example Readings - overview & key concepts Glance, Arregui & Dardenne (1997)

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Collaborative Filtering and Recommender Systems

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  1. Collaborative Filtering and Recommender Systems Brian Lewis INF 385Q Knowledge Management Systems November 10, 2005

  2. 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

  3. 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)

  4. Recommender systems defined • Systems that evaluate quality based on the preferences of others with a similar point of view

  5. Hobo symbols from http://www.slackaction.com/signroll.htm

  6. Hobo symbols as RS? • Specific to a community • Implicit and explicit signs • Filtered through encoding • Cold-start problem?

  7. Compare to today • Recommend • Don't recommend

  8. 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

  9. Glance, Arregui & Dardenne (1997) • User-item matrix of ratings

  10. 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

  11. 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

  12. 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

  13. 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

  14. 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?

  15. 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

  16. 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

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

  18. 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.

  19. Questions Questions live here

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