1. Recommender Systems

6. >1,000,000,000

7. Finding Trusted Information How many cows in Texas? http://www.cowabduction.com/

8. Outline What are Recommender Systems? How do they work? How can we integrate social information / trust? What are some applications?

9. Netflix

10. Amazon

13. How do they work? Two main methods Find things people similar to me like Find things similar to the things I like

14. Example with People Who is a better predictor for Alice? Compute the correlation: Bob 0.26 Chuck: 0.83 Recommend a rating of “Vertigo” for Alice Bob rates it a 3 Chuck rates it a 5 (0.26 * 3 + 0.82 * 5) / (0.26+ 0.82) = 4.5

15. Item similarity Methods are more complex Computed using features of items E.g. genre, year, director, actors, etc. Some sites use a very nuanced set of features

18. How good is a Recommender System? Generally: error Error = My rating - Recommended Rating Do this for all items and take the average Need alternative ways of evaluating systems Serendipity over accuracy Diversity

19. Trust in Recommender Systems If we have a social network, can we use it to build trusted recommender systems? Where does the trust come from? How can we compute trust? Some example applications

20. 1. Your Favorite Movie  2. Your Least Favorite Movie  3. Some Mediocre Movie  4. Some Mediocre Movie  5. Some Mediocre Movie  6. Some Mediocre Movie  7. Some Mediocre Movie  8. Some Mediocre Movie  9. Some Mediocre Movie  10. Some Mediocre Movie  In-Class Exercise

21. Sample Profile Knowing this information, how much do you trust User 7 about movies?

22. Factors Impacting Trust Overall Similarity Similarity on items with extreme ratings Single largest difference Subject’s propensity to trust

23. Propensity to Trust

24. Advogato Peer certification of users Master : principal author or hard-working co-author of an "important" free software project Journeyer: people who make free software happen Apprentice: someone who has contributed in some way to a free software project, but is still striving to acquire the skills and standing in the community to make more significant contributions Advogato trust metric applied to determine certification

25. Advogato Website http://www.advogato.org/ Certifications are used to control permissions Only certified users have permission to comment Combination of certifications and interest ratings of users’ blogs are used to filter posts

27. MoleSkiing http://www.moleskiing.it/ (note: in Italian) http://www.moleskiing.it/ Ski mountaineers provide information about their trips Users express their level of trust in other users The system shows only trusted information to every user Uses MoleTrust algortihm

30. FilmTrust Movie Recommender Website has a social network where users rate how much they trust their friends about movies Movie recommendations are made using trust Recommended Rating = Weighted average of all ratings, where weight is the trust (direct or inferred) that the user has for the rater

33. Challenges Can these kinds of approaches create problems? Recommender Systems - recommending items that are too similar Trust-based recommendations - preventing the user from seeing other perspectives

34. Conclusions Recommender systems create personalized suggestions to users Social trust is another way of personalizing content recommendations Connect social relationships with online content to highlight the most trustworthy information Still many challenges to doing this well