Carrots for Couch Potatoes: Improving recommendations by motivating the crowd
•Download as PPTX, PDF•
2 likes•486 views
Some recommender systems exploit quite complex features and give users the impression that black magic is performed to provide somewhat meaningful recommendations. Users may moreover adopt a rather passive attitude: instead of actively interacting with the recommendations and providing feedback they take the quality of the recommendations for granted. In this talk, we discuss challenges of crowd-based recommender systems and present some strategies for involving people more actively into the flow of computing recommendations.
Recommended
Recommended
More Related Content
Similar to Carrots for Couch Potatoes: Improving recommendations by motivating the crowd
Similar to Carrots for Couch Potatoes: Improving recommendations by motivating the crowd (20)
Recently uploaded
Recently uploaded (20)
Carrots for Couch Potatoes: Improving recommendations by motivating the crowd
- 1. Carrots for Couch Potatoes Improving recommendations by motivating the crowd @fabianabel
- 2. Definition 1 “Recommender system = black box that knows the answer to the ultimate question…of life, the universe and everything.” Hypothesis 1 “The more obscure a recommender system, the higher the chance that its users are happy with the system.”
- 3. Definition 2.1 “Data Scientist = folks that can program the smartest recommender systems.” Hypothesis 2 “Nobody needs an interaction designer.” Definition 2.2 “Interaction Designer = folks that think about what users actually want to do.”
- 4. Definition 3 “Couch potatoes = users who do not provide input to a recommender system, but have high expectations towards the quality of the system.” Hypothesis 3 “The quality of a recommender system increases with the number of couch potatoes that are *using* the system.”
- 5. Goals of Recommender Systems Make users happy and surprise them with new and relevant content. [user perspective] Deliver content so that monetary success of the business is maximized. [business perspective]
- 6. Problem space Challenges • Understanding the users • Understanding the items • Coding a good (ensemble of) recommendation algorithm(s) • Evaluation • Presentation of recommendations • … recommender system users items recommender system
- 8. Delete item Hide entire box
- 9. (1) Less-Like-This (2) Collaborative filtering deletions? Clicks + bookmarks (1) More-Like-This (2) Collaborative filtering positive feedback interactions exploited by… negative feedback
- 10. AB test* resultsCTR Control group Group with Less-Like-This filtering -3% ? *AB tests on XING - are done in front-end and back-end components - typically 50:50 random splits (others: specific groups; inter-leaving) - Run for days to weeks significance level: p-value < 0.01 - Validation includes AA comparison, BA/BA test, repeating AB test
- 11. More cookies! People used “delete” to get more recommendations.
- 12. Hypothesis 2 is wrong! Hypothesis 2 “Nobody needs an interaction designer.”
- 13. How can we collect more valuable explicit feedback from our couch potatoe users?
- 14. Related Work
- 15. 1. First explicit feedback is collected right from the beginning during on-boarding, e.g.: select 3 favorites rate 10 items (5-star rating scale) 2. Continuous collection of explicit feedback user control, e.g.: ratings (lightweight) revising ratings, taste preference questionnaire (advanced)
- 16. 3. Understanding why a user liked or disliked an item, e.g.: emphasizing topics blocking topics
- 17. Explicit feedback is key! 1. From the beginning 2. Continuously 3. Understanding why…
- 18. Hypothesis 3 is wrong! Hypothesis 3 “The quality of a recommender system increases with the number of couch potatoes that are *using* the system.”
- 19. We need to motivate our couch potatoes to contribute to improve our recommender sytems!
- 20. Feedback app
- 21. Only means of motivation: 1. Promise: “this will enhance your recommendations” 2. Progress bar
- 22. Feedback App Pros • Continuous stream of explicit feedback • Decoupled from the actual system Cons • Attracts “Haters” more than fans • Decoupled from the actual system In addition, we also want feedback mechanisms that are more integrated into the natural interaction flow of the system.
- 23. explanations feedback Is this job for me?
- 24. Is Hypothesis 1 wrong as well? Hypothesis 1 “The more obscure a recommender system, the higher the chance that its users are happy with the system.”
- 25. Conclusions