Recsys2011 presentation "Each to his own - How Different Users Call for Different Interaction Methods"
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Recsys2011 presentation "Each to his own - How Different Users Call for Different Interaction Methods"
- 1. !"#$%&'(!$")'"*)+$&,-(.%)/+!."+./ How Different Users Call for Different Interaction Methods EACH TO
- 2. PEOPLE DIFFER IN WHAT THEY LIKE
- 3. PEOPLE DIFFER IN WHAT THEY LIKE recommender system: computing ....... likes blue likes purple
- 4. MORE DIFFERENCES... Recommendations are tailored... ...but the interaction is the same for every user People also differ in how they make decisions! They may need different ways of interacting with the system
- 5. TAILORED INTERFACES We take a closer look at these differences... On what characteristics do users differ? Do they use different decision strategies? ...and tailor the system to these differences Which interaction methods support these strategies?
- 6. DECISION STRATEGIES A decision strategy is a procedure for making decisions Weigh the attribute values Pick the very ?rst item you see Different interaction methods may support these strategies to a different extent
- 7. PERSONAL DIFFERENCES The decision strategy selected by the user depends on her personal characteristics Bettman, Luce & Payne, 1998 Typical characteristics: Experts vs. Novices (Alba & Hutchinson, 1987; Coupey et al., 1998) Distrusting vs. Trusting (Vries, 2004; Wang & Benbasat, 2007)
- 8. IN SHORT... Different user Different decision Different interaction
- 11. SETUP OF EXPERIMENT pre post ? ? ? ? ? ?
- 12. THE SYSTEM
- 13. THE SYSTEM
- 14. THE SYSTEM
- 15. THE SYSTEM
- 16. FIVE INTERACTION METHODS TopN: the 10 most popular measures Baseline condition; not personalized, virtually no interaction Get more recommendations by classifying measures
- 17. FIVE INTERACTION METHODS Sort: sort the measures by any attribute Implements the lexicographic strategy: ? Select the most important attribute ? Choose the item with the highest value on this attribute Users can (re-)sort by clicking on table headers
- 18. FIVE INTERACTION METHODS Explicit: a typical MAUT recommender Implements the weighted adding strategy ? Normalize the attribute values: vij ? Assign weights to the attributes: wj ? Multiply and sum to get a utility: Ui = ¡Ævij* wj ? Choose item with highest utility
- 19. FIVE INTERACTION METHODS Implicit: system decides on the weights User behavior is analyzed to update the weights Update rules based on previous versions of the system Weights are not shown
- 20. FIVE INTERACTION METHODS Hybrid: Explicit + Implicit Both the system and the user can update the weights
- 21. PARTICIPANTS 147 participants (158 at ?rst, 11 removed due to very short interaction time) Recruited by an external company 79 male, 68 female Average age: 40 (sd: 15.9) 29 students, 93 working, 25 retired 23 high school, 24 intermediate degree, 53 college, 47 grad
- 22. PERSONAL CHARACTERISTICS Domain knowledge: Experts vs. Novices ? 7 items, e.g. ¡°I understand the difference between energy saving measures¡± Trusting propensity: Distrusting vs. Trusting ? 6 items, e.g. ¡°In general, most folks keep their promises¡± Persistence: Satis?cers vs. Maximizers ? 4 items, e.g. ¡°I am willing to examine the product attributes very carefully in order to make sure that the
- 23. USER EXPERIENCE Control: Does it support my strategy? ? 7 items, e.g. ¡°I had full control over the system¡± Understandability: Is it confusing? ? 8 items, e.g. ¡°I understand the system¡± Trust in the system: Is it fair to me? ? 4 items, ¡°The system is not biased¡±
- 24. USER EXPERIENCE QUIS: Is the user interface usable? ? 5 items, summed 9-point scale Perceived system effectiveness: Is it useful? ? 5 items, ¡°I make better choices with this system¡± Choice satisfaction: Do I like what I chose? ? 4 items, ¡°I think I chose the best measures¡±
- 26. DOMAIN Differences between experts and novices !"#$%&'(!$")'"*)+$&,-(.%)/+!."+./
- 27. DOMAIN KNOWLEDGE Novices may like TopN, Sort and Implicit because they lack attribute knowledge Implicit may be more confusing Experts may like Explicit and Hybrid because they can leverage their attribute knowledge, and because
- 28. DOMAIN KNOWLEDGE Novices like 2! the TopN *! system 1! TopN! Control! Sort! 0! -2! -1! 0! 1! 2! Explicit! Implicit! Hybrid! -1! -2! Domain Knowledge!
- 29. DOMAIN KNOWLEDGE Novices like 2! the TopN *! system 1! TopN! They perceive Control! Sort! more control in -2! -1! 0! 0! 1! 2! Explicit! this system than Implicit! experts -1! Hybrid! -2! Domain Knowledge!
- 30. DOMAIN KNOWLEDGE Novices like 2! ***! Perceived system effectiveness! the TopN system 1! 1! **! TopN! They perceive Sort! more control in -2! -1! 0! 0! 1! 2! Explicit! this system than Implicit! experts Hybrid! -1! They ?nd it far more effective -2! Domain Knowledge!
- 31. DOMAIN KNOWLEDGE Novices like 2! ***! Perceived system effectiveness! the TopN system 1! 1! **! TopN! They perceive Sort! more control in -2! -1! 0! 0! 1! 2! Explicit! this system than Implicit! experts Hybrid! -1! They ?nd it far more effective -2! Domain Knowledge!
- 32. DOMAIN KNOWLEDGE Novices like the TopN system They perceive more control in this system than experts They ?nd it far more effective
- 33. DOMAIN KNOWLEDGE Experts like the 2! Hybrid system Understandability! 1! *! TopN! Sort! 0! -2! -1! 0! 1! 2! Explicit! Implicit! Hybrid! -1! -2! Domain Knowledge!
- 34. DOMAIN KNOWLEDGE Experts like the 2! Hybrid system They understand it Understandability! 1! *! TopN! Sort! 0! -2! -1! 0! 1! 2! Explicit! Implicit! Hybrid! -1! -2! Domain Knowledge!
- 35. DOMAIN KNOWLEDGE Experts like the 45! Hybrid system 40! User interface satisfaction! 35! They understand it 30! ***! They are more 25! TopN! Sort! satis?ed with its UI Explicit! 20! **! Implicit! 15! Hybrid! 10! 5! -2! -1! 0! 1! 2! Domain Knowledge!
- 36. DOMAIN KNOWLEDGE Experts like the ***! 2! Perceived system effectiveness! Hybrid system They understand it 1! 1! **! They are more TopN! Sort! satis?ed with its UI -2! -1! 0! 0! 1! 2! Explicit! They ?nd it more Implicit! Hybrid! effective -1! -2! Domain Knowledge!
- 37. DOMAIN KNOWLEDGE Experts like the ***! 2! Perceived system effectiveness! Hybrid system They understand it 1! 1! **! They are more TopN! Sort! satis?ed with its UI -2! -1! 0! 0! 1! 2! Explicit! They ?nd it more Implicit! Hybrid! effective -1! Control of Explicit -2! and convenience of Domain Knowledge!
- 38. DOMAIN KNOWLEDGE Experts like the Hybrid system They understand it They are more satis?ed with its UI They ?nd it more effective Control of Explicit and convenience of
- 40. DOMAIN KNOWLEDGE Choices 2! *! 1! Experts make better decisions with Choice satisfaction! 1! *! Explicit, Implicit TopN! and Hybrid 0! Sort! ? But only Hybrid is -2! -1! 0! 1! 2! Explicit! Implicit! better overall Hybrid! -1! -2! Domain Knowledge!
- 41. DOMAIN KNOWLEDGE Choices 2! *! 1! Experts make better decisions with Choice satisfaction! 1! *! Explicit, Implicit TopN! and Hybrid 0! Sort! ? But only Hybrid is -2! -1! 0! 1! 2! Explicit! Implicit! better overall Hybrid! Novices make better -1! decisions with TopN* -2! ? Unable to leverage Domain Knowledge!
- 42. DOMAIN KNOWLEDGE Choices Experts make better decisions with Explicit, Implicit and Hybrid ? But only Hybrid is better overall Novices make better decisions with TopN* ? Unable to leverage
- 43. TRUSTING Differences between people who trust the system !"#$%&'(!$")'"*)+$&,-(.%)/+!."+./
- 44. TRUSTING PROPENSITY Trust is necessary to accept the recommendations A lack of trust can cause reactance Users have an initial trusting propensity Distrusting users may not like Implicit because they need a system that is
- 45. TRUSTING PROPENSITY Distrusting 45! users dislike 40! User interface satisfaction! 35! Explicit, 1! Implicit, TopN 30! *! TopN! 25! Sort! Explicit! 20! Implicit! ****! 15! Hybrid! 10! 5! -2! -1! 0! 1! 2! Trusting propensity!
- 46. TRUSTING PROPENSITY Distrusting 45! users dislike 40! User interface satisfaction! 35! Explicit, 1! Implicit, TopN 30! *! TopN! 25! Sort! They are not 20! Explicit! satis?ed with the Implicit! UI ****! 15! Hybrid! 10! 5! -2! -1! 0! 1! 2! Trusting propensity!
- 47. TRUSTING PROPENSITY Distrusting 2! users dislike Explicit, Perceived system effectiveness! 1! Implicit, TopN TopN! Sort! 0! They are not -2! -1! 0! 1! 2! Explicit! 1! satis?ed with the Implicit! Hybrid! UI -1! They do not ?nd *! these systems *! -2! effective Trusting propensity!
- 48. TRUSTING PROPENSITY Distrusting users dislike Explicit, Implicit, TopN They are not satis?ed with the UI They do not ?nd these systems effective
- 49. PERSISTENCE Differences between satis?cers and maximizers !"#$%&'(!$")'"*)+$&,-(.%)/+!."+./
- 50. PERSISTENCE Satis?cers may like Implicit The system updates the recommendations to provide similar items Maximizers may like Implicit or TopN More counterfactual thinking, more anticipated post-decision regret This is aggravated in systems with
- 52. PERSISTENCE 2! Choices **! Maximizers are 1! more satis?ed with Choice satisfaction! their choices! TopN! Sort! 0! -2! -1! 0! 1! 2! Explicit! Implicit! Hybrid! -1! -2! Persistence!
- 53. PERSISTENCE 2! Choices **! Maximizers are 1! more satis?ed with Choice satisfaction! their choices! TopN! Sort! Maximizers like -2! -1! 0! 0! 1! 2! Explicit! their choices in Implicit! TopN -1! Hybrid! -2! Persistence!
- 54. PERSISTENCE 2! Choices **! Maximizers are 1! more satis?ed with Choice satisfaction! their choices! TopN! Sort! Maximizers like -2! -1! 0! 0! 1! 2! Explicit! their choices in Implicit! TopN -1! Hybrid! Satis?cers like their choices in -2! Implicit* Persistence!
- 55. PERSISTENCE Choices Maximizers are more satis?ed with their choices! Maximizers like their choices in TopN Satis?cers like their choices in Implicit*
- 56. !"#$%&'(!$")'"*)+$&,-(.%)/+!."+./ Implications for real-world recommender systems CONCLUSIO
- 57. FIRST SOME RESERVATIONS... Small sample of users 28-33 participants per condition; low power Domain encourages multiple decisions; dampens the effects Results pertain to attribute-based systems Does not apply to collaborative ?ltering
- 58. CONCLUSIONS Hybrid is better than Explicit and Implicit For experts: tweak preferences: convenience and control For distrusting users: negative reactions to other systems However, TopN may be better in some cases For novices: no knowledge to exploit the bene?ts
- 59. HOW TO COMBINE TOP-N AND HYBRID? Spatially separate them In different sections of the interface Temporally separate them Start with the TopN, carefully introduce implicit recommendations, then introduce explicit controls Assign the correct method to each user Discover the user¡¯s characteristics, then tailor the interface to her speci?c needs
- 60. MORE IN GENERAL... Each to his own The best interaction method depends on user characteristics Taking these into account may result in signi?cantly better recommender systems
- 61. THANKS TO... Niels Reijmer Martijn Willemsen
- 62. !"#$%&'(!$")'"*)+$&,-(.%)/+!."+./ bart.k@uci.edu @usabart www.usabart.nl THANK YOU
Editor's Notes
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- #28: This is what people here usually see as a baseline (aside from random, the worst possible)\n
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- #67: We did not expect this for explicit and TopN\nNo differences in control or understandability\n
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- #71: We did not expect this for explicit and TopN\nNo differences in control or understandability\n
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- #74: We did not expect this for explicit and TopN\nNo differences in control or understandability\n
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