Group Recommendations: Enhancing Decision-making in Interrelated User Groups

1. What Difference a Group MakesWeb-Based Recommendations for Interrelated Users Tomek Loboda Anthony Jameson and Barry Smyth

2. Additional Papers • Adaptive Radio: Achieving Consensus Using Negative Preferences By Dennis L. Chao, Justin Balthrop, Stephanie Forrest In  Proceedings of the 2005 International ACM SIGGROUP Conference on Supporting Group Work, New York, 2005. At  http://www.cs.unm.edu/~dlchao/papers/chao05group.pdf • Explaining Collaborative Filtering Recommendations By  Jonathan L. Herlocker, Joseph A. Konstan, and John Riedl In  Proceedings of the ACM 2000 Conference on Computer Supported Cooperative Work, December 2-6, 2000. At  http://www.grouplens.org/papers/pdf/explain-CSCW.pdf

3. Outline • Introduction • Preferences Specification • Preferences Aggregation • Explaining Recommendations • Final Decision • Conclusions

4. Introduction

5. I Scenarios • Group of friends going on vacation together • A family selecting movie or TV show to watch together • A group of colleagues choosing a restaurant for an evening out • Keyword together

6. I Issues: Phase 1 • Members specify their preference • It may be desirable for members to examine each other’s preference specification • What benefits and drawbacks can such examination have and how can it be supported by the system?

7. I Issues: Phase 2 • The system generates recommendations • Some procedures for aggregating preferences must be applied • What conditions to such aggregation procedures have to be fulfilled and what kind of procedures tend to fulfill them?

8. I Issues: Phase 3 • The system presents recommendations to the members • The (possibly different) suitability of a solution for the individual members becomes an important aspect of solution • How can relevant information about suitability for individual members be presented effectively?

9. I Issues: Phase 4 • Members decide which recommendation (if any) to accept • The final decision is not necessarily made by a single person – negotiation may be required • How can the system support the process of arriving at a final decision, in particular when members cannot engage in a face-to-face discussion?

10. I vs Collaborative Filtering • CF model groups of users… • …using similarity measure, i.e. shared: • Preferences • Rating patterns • etc. • GR model groups of users… • …defined by a social context: • Potentially much less similarities

11. I Systems • Let’s Browse  browsing the Web • PolyLens  movie recommender • Intrigue tour guide assistant • MusicFX automatic music selection • Travel Decision Forum vacation planner • I-Spy Web search engine • Adaptive Radio music broadcast in a shared environment

12. 1 Preferences Specification

13. PS Methods • Implicit – whenever possible • Explicit – sometimes unavoidable • MusicFX  rating 91 genres • Travel Decision Forum  rating variety of attributes of vacations destinations • Adaptive Radio  censoring disliked songs • I-Spy  selecting a link

14. PS Sharing Preferences • Saving effort • Learning from other members “I can’t go hiking, because of an injury” • Travel Decision Forum  copy + edit

15. PS Travel Decision Forum 

16. PS Sharing Preferences • Taking into account attitudes and anticipated behavior of other members • Encouraging assimilation to facilitate the reaching of agreement

17. 2 Preferences Aggregation

18. PA Individual Models • For each candidate c • For each member m predict the rating rcm • Compute an aggregate rating Rc • Recommend the set of candidates with the highest predicted ratings Rc • Rc = max rcm

19. PA Group Model • Compute an aggregate preference model M that represents the preferences of the group as a whole. • For each candidate c use M to predict the rating Rc for the group as a whole. • Recommend the set of candidates with the highest predicted ratings Rc.

20. PA Group Model: Details • Preferences defined and negotiated once • Privacy issue avoided • Recommending (accurately or not) a candidate for which the predicted rating of each individual member was low

21. PA Goals and Procedures • Maximizing average satisfaction • Minimizing misery • Ensuring some degree of fairness • Discouraging manipulation of the recommendation mechanism (…) • Ensuring some degree of comprehensibility • Treating different group members differently (where appropriate)

22. PA Counteracting Manipulation • Not showing preferences of other’s before specifying own ones: • Guessing (at least roughly) • Advantages of showing them • Manipulation can most likely happen… • …with explicit preference specification as aninput

23. PA Counteracting Manipulation Solution… • …explicit model with trust factor • I-Spy: • Clicking on a link promotes it – easy to observe and abuse • Each link selection action is evaluated for reliability • No promotion unless a threshold is reached

24. PA Counteracting Manipulation • Inherently nonmanipulable mechanism? • Automatic generation? • Travel Decision Forum: • Median of the individual preferences used as a preference of the group as a whole • Automatic generation on the fly

25. PA Right Procedure – Levels • By designers – before deployment: • System’s goals/assumptions/context driven • PolyLens  small groups  “least misery” aggregation function • Avoiding manipulation not always necessary: • Purchasing decisions as input

26. PA Right Procedure – Levels • By users – aggregation function selection: • Before any recommendations are made • During an iterative process of requesting recommendations • Intrigue different weights for subgroups • Travel Decision Forum variety of aggregation mechanisms

27. PA Right Procedure – Levels • By users – specific recommendation consideration: • User compiles the recommendations with the goal in mind before making the final decision • The system should take those goals into account too to ensure that the set of candidates includes one or more highly suitable option

28. PA Multiple Decisions • Larger set of decisions at the same time or in succession • Intrigue several sights to visit • Let’s Browse number of web pages in the course of a given session • Local vs. Global: • L: none of the members satisfied • G: each member satisfied some of the time • Decisions as a package

29. 3 Explaining Recommendations

30. ER Motivation • Black box does not provide insight into: • How the recommendations were arrived at • How attractive they are for each individual member • Explanations: • Provide transparency • Helps to detect sources of errors • Important especially in high-risk domains

31. ER Benefits for the User • Justification – how much confidence will I place in that recommendation • User Involvement – user adds their knowledge and inference skills • Education – better understanding the strengths and limitations • Acceptance – as a decision support tool

32. ER Let’s Browse

33. ER PolyLens G I

34. ER Intrigue

35. ER I-Spy • How other community members have dealt with a given page • Information offered: • Related queries • Quantitative and temporal information

36. ER Travel Decision Forum

37. ER Travel Decision Forum • Simulated reaction effects: • Increased awareness of other members’ point of view • Overcoming the natural tendency to focus on one’s own evaluations

38. 4 Final Decision

39. FD Considerations • The decision is not made by a single person and therefore… • …extensive debate and negotiations may be required • Can members communicate easily?

40. FD Existing Approach 1 • Autonomous translation of most highly rated solutions into actions • MusicFX changes the channel • Adaptive Radio changes the song • Good when a quick decision is needed

41. FD Existing Approach 2 • Onegroup member is responsible for making the final decision • Let’s Browse one person is controlling the pointing device • Intrigue tourist guide is deciding what tour should be taken • Makes the system design simpler

42. FD Existing Approach 3 • Conventional conversation (face-to-face or by phone) as a medium • PolyLens members can call/IM each other • Makes the system design simpler

43. FD Existing Approach 4 • Built-in communication support • Travel Decision Forum  avatars can be granted a certain amount of authority to accept proposals during interactions with other members • Makes the system design difficult

44. FD Possible Extensions • Thresholds of acceptance • Voting • Isn’t voting itself recommending? • Seeing or not seeing votes of others • Counting and weighting votes • Presenting the results of voting • How to arrive at the final decision • Yes, but in a much simpler context… • …which means it is better defined

45. Conclusions!

46. C The 4 Phases • Preferences Specification • Preferences Aggregation • Explaining Recommendations • Final Decision

47. C Group Recommenders • Only few systems • Small subset of possible recommendation techniques • Limited number of application domains • Different superficially only • Context dependant

48. C Other Systems • What happens if… • …we have groups of cooperating, information seeking user? • We can see how would the 4 phases be applied • For instance, adaptive navigation support

49. C I-Spy Demo?

50. Thank you!