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) Konstan, Miller, et al. (1997) Proctor & McKinlay (1997) Conclusions References 2

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) 3

Recommender systems defined

 Systems that evaluate quality based on the preferences of others with a similar point of view 4

Hobo symbols from http://www.slackaction.com/signroll.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 7

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 8

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

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 10

Konstan, Miller, et al. (1997)  Predictive utility   Risk - costs of misses and false 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 11

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 12

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 13

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 15

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 16

Conclusions

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

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 19