Recommender Systems

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. In-Class Exercise • Your Favorite Movie • Your Least Favorite Movie  • Some Mediocre Movie  • Some Mediocre Movie  • Some Mediocre Movie  • Some Mediocre Movie  • Some Mediocre Movie  • Some Mediocre Movie  • Some Mediocre Movie  • Some Mediocre Movie 

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) • 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