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Recommender Systems

Recommender Systems

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Recommender Systems

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  1. Recommender Systems

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

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

  4. Netflix

  5. Amazon

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

  7. Example with People • Who is a better predictor for Alice? • Compute the correlation: • Bob 0.26 • Chuck: 0.82 • 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

  8. 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

  9. Music DNA • arrangement - the selection and adaptation of a composition or parts of a composition to instruments for which it was not originally designed • beat - the regular pulse of music • form - the structure of a composition, the frame upon which it is constructed; based upon repetition, contrast, and variation • harmony - the concordant (or consonant) combination of notes sounded simultaneously to produce chords • lyrics - the words of a song • melody - a succession of tones comprised of mode, rhythm, and pitches so arranged as to achieve musical shape

  10. More DNA • orchestration - the art of arranging a composition for performance by an instrumental ensemble • rhythm - the subdivision of a space of time into a defined, repeated pattern • syncopation - deliberate upsetting of the meter or pulse of a composition by means of a temporary shifting of the accent to a weak beat or an off-beat • tempo - the speed of the rhythm of a composition • vamping - to extemporize the accompaniment to a solo voice or instrument • voice - the production of sound from the vocal chords, often used in music; falls into six basic categories defined by pitch, ranging, from bottom to top, Bass, Baritone, Tenor, Contralto, Mezzo Soprano, and Soprano

  11. 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

  12. 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

  13. 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 

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

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

  16. 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

  17. 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

  18. 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

  19. 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

  20. 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

  21. 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