Ronny lempelyahooindiabigthinkerapril2013

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Ronny lempelyahooindiabigthinkerapril2013

  1. 1. RecommendationChallenges in Web Media Settings Ronny Lempel Yahoo! Labs, Haifa, Israel
  2. 2. Recommender Systems • Pioneered in the mid/late 90s by Amazon • Today applied “everywhere” • Shopping sites • Content sites (news, sports, gossip, …) • Multimedia streaming services (videos, music) • Social networks • Easily merit a dedicated academic course -1- Bangalore/MumbaiConfidential Yahoo! 2013
  3. 3. Recommendation in Social Networks -2- Bangalore/MumbaiConfidential Yahoo! 2013
  4. 4. Recommender Systems – Example of Effectiveness • 1988: Random House releases “Touching the Void”, a book by a mountain climber detailing a harrowing account of near death in the Andes – It got good reviews but modest commercial success • 1999: “Into Thin Air”, another mountain-climbing tragedy book, becomes a best-seller • By virtue of Amazon’s recommender system, “Touching the Void” started to sell again, prompting Random House to rush out a new edition – A revised paperback edition spent 14 weeks on the New York Times bestseller list From “The Long Tail”, by Chris Anderson -3- Bangalore/MumbaiConfidential Yahoo! 2013
  5. 5. The Netflix ChallengeSlides 4-6 courtesy ofYehuda Koren, memberof Challenge winners“Bellkor’s PragmaticChaos” -4- Bangalore/MumbaiConfidential Yahoo! 2013
  6. 6. “We’re quite curious, really. To the tune ofone million dollars.” – Netflix Prize rules • Goal was to improve on Netflix’ existing movie recommendation technology • The open-to-the-public contest began October 2, 2006; winners announced September 2009 • Prize – Based on reduction in root mean squared error (RMSE) on test data – $1 million grand prize for 10% improvement on Cinematch result – $50K 2007 progress prize for 8.43% improvement – $50K 2008 progress prize for 9.44% improvement • Netflix gets full rights to use IP developed by the winners – Example of Crowdsourcing – Netflix basically got over 100 researcher years (and good publicity) for $1.1M -5- Bangalore/MumbaiConfidential Yahoo! 2013
  7. 7. Netflix Movie Ratings Data Training data Test data• Training data user movie score user movie – 100 million 1 21 1 1 62 ? ratings 1 213 5 1 96 ? – 480,000 users 2 345 4 2 7 ? – 17,770 movies – 6 years of data: 2 123 4 2 3 ? 2000-2005 2 768 3 3 47 ?• Test data 3 76 5 3 15 ? – Last few ratings 4 45 4 4 41 ? of each user (2.8 5 568 1 4 28 ? million) 5 342 2 5 93 ?• Dates of ratings are 5 234 2 5 74 ? given 6 76 5 6 69 ? 6 56 4 6 83 ? -6- Bangalore/MumbaiConfidential Yahoo! 2013
  8. 8. Recommender Systems – Mathematical Abstraction • Consider a matrix R of users and the items they’ve consumed – Users correspond to the rows of R, products to its columns, with ri,j=1 whenever person i consumed item j – In other cases, ri,j might be the rating given by person i on item j • The matrix R is typically very sparse Items – …and often very large • Real-life task: top-k recommendation users – From among the items that weren’t R= consumed by each user, predict which ones the user would most enjoy • Related task on ratings data: matrix completion |U| x |I| – Predict users’ ratings for items they have yet to rate, i.e. “complete” missing values -7- Bangalore/MumbaiConfidential Yahoo! 2013
  9. 9. Types of Recommender Systems At a high level, two main techniques: • Content-based recommendation: characterizes the affinity of users to certain features (content, metadata) of their preferred items – Lots of classification technology under the hood • Collaborative Filtering: exploits similar consumption and preference patterns between users – See next slides • Many state of the art systems combine both techniques -8- Bangalore/MumbaiConfidential Yahoo! 2013
  10. 10. Collaborative Filtering – Neighborhood Models • Compute the similarity of items [users] to each other – Items are considered similar when users tend to rate them similarly or to co-consume them – Users are considered similar when they tend to co-consume items or rate items similarly • Recommend to a user: – Items similar to items he/she has already consumed [rated highly] – Items consumed [rated highly] by similar users • Key questions: – How exactly to define pair-wise similarities? – How to combine them into quality recommendations? -9- Bangalore/MumbaiConfidential Yahoo! 2013
  11. 11. Collaborative Filtering – Matrix Factorization • Latent factor models (LFM): – Maps both users and items to some f-dimensional space Rf, i.e. produce f-dimensional vectors vu and wi for each user and items – Define rating estimates as inner products: qij = <vi,wj> – Main problem: finding a mapping of users and items to the latent factor space that produces “good” estimates – Closely related to dimensionality reduction techniques of the ratings matrix R (e.g. Singular Value Decomposition) Items V W users R= ≈ |U| x |I| |U| x f f x |I| - 10 - Bangalore/MumbaiConfidential Yahoo! 2013
  12. 12. Web Media Sites - 11 - Bangalore/MumbaiConfidential Yahoo! 2013
  13. 13. Challenge: Cold Start Problems • Good recommendations require observed data on the user being recommended to [the items being recommended] – What did the user consume/enjoy before? – Which users consumed/enjoyed this item before? • User cold start: what happens when a new user arrives to a system? – How can the system make a good “first impression”? • Item cold start: how do we recommend newly arrived items with little historic consumption? • In certain settings, items are ephemeral – a significant portion of their lifetime is spent in cold-start state – E.g. news recommendation - 12 - Bangalore/MumbaiConfidential Yahoo! 2013
  14. 14. Low False-Positive Costs False positive: recommending an irrelevant item • Consequence, in media sites: a bit of lost time – As opposed to lots of lost time or money in other settings • Opportunity: better address cold-start issues • Item cold-start: show new item to select group of users whose feedback should help in modeling it to everyone – Note the very short item life times in news cycles • User cold-start: more aggressive exploration – Vs. playing it safe and perpetuating popular items • Search: injecting randomization into the ranking of search results (Pandey et al., VLDB 2005) - 13 - Bangalore/MumbaiConfidential Yahoo! 2013
  15. 15. Challenge: Inferring Negative Feedback • In many recommendation settings we only know which items users have consumed, not whether they liked them – I.e. no explicit ratings data • What can we infer about satisfaction of consumed items from observing other interactions with the content? – Web pages: what happens after the initial click? – Short online videos: what happens after pressing “play”? – TV programs: zapping patterns • What can we infer about items the user did not consume? • Was the user even aware of the items he/she did not consume? – What items did the recommender system expose the user to? - 14 - Bangalore/MumbaiConfidential Yahoo! 2013
  16. 16. Presentation Bias’ Effect on Media Consumption • Pop Culture: items’ longevity creates familiarity • Media sites: items are ephemeral, and users are mostly unaware of items the site did not expose them to • Presentation bias obscures users’ true taste – they essentially select the best of the little that was shown • Must correctly account for presentation bias when modeling: seen and not selected ≠ not seen and not selected • Search: negative interpretation of “skipped” search results (Joachims, KDD’2002) - 15 - Bangalore/MumbaiConfidential Yahoo! 2013
  17. 17. Layouts of Recommendation Modules • Interpreting interactions in vertical layouts is “easy” using the “skips” paradigm • What about 2D, tabbed, horizontal layouts? - 16 - Bangalore/MumbaiConfidential Yahoo! 2013
  18. 18. Layouts of Recommendation Modules • What about multiple presentation formats? - 17 - Bangalore/MumbaiConfidential Yahoo! 2013
  19. 19. Personalized PopularContextual - 18 - Bangalore/MumbaiConfidential Yahoo! 2013
  20. 20. Contextualized, Personalization, Popular • Web media sites often display links to additional stories on each article page – Matching the article’s context, matching the user, consumed by the user’s friends, popular • When creating a unified list for a given a user reading a specific page, what should be the relative importance of matching the additional stories to the page vs. matching to the user? • Ignoring story context might create offending recommendations • Related direction: Tensor Factorization, Karatzoglou et. al, RecSys’2010 - 19 - Bangalore/MumbaiConfidential Yahoo! 2013
  21. 21. Challenge: Incremental Collaborative Filtering • In a live system, we often cannot afford to recompute recommendations regularly over the entire history • Problem: neither neighborhood models nor matrix factorization models easily lend themselves to faithful incremental processing User-Item User-Item User-Item Mi = CF-ALG(ti) Interactions Interactions Interactions t1 t2 t3 ∀f, f { M1, M2 } ≠ CF_ALG(t1∪t2) … T • Is there a model aggregation function f(Mprev, Mcurr) that is “good enough”? - 20 - Bangalore/MumbaiConfidential Yahoo! 2013
  22. 22. Challenge: Repeated Recommendations • One typically doesn’t buy the same book twice, nor do people typically read the same news story twice • But people listen to the songs they like over and over again, and watch movies they like multiple times as well • When and how frequently is it ok to recommend an item that was already consumed? • On the other hand, when should we stop showing a recommendation if the user doesn’t act upon it? • Implication: a recommendation system may not only need to track aggregated consumption to-date, – It may need to track consumption timelines – It may need to track recommendation history - 21 - Bangalore/MumbaiConfidential Yahoo! 2013
  23. 23. Challenge: Recommending Sets & Sequences ofItems • In some domains, users consume multiple items in rapid succession (e.g. music playlists) – Recent works: WWW’2012 (Aizenberg et al., sets) and KDD’2012 (Chen et al., sequences) • From Independent utility of recommendations to set or sequence utility, predicting items that “go well together” – Sometimes need to respect constraints • Tiling recommendations: in TV Watchlist generation, the broadcast schedules further complicates matters due to program overlaps • Perhaps a new domain of constrained recommendations? • Search: result set attributes (e.g. diversity) in Search (Agrawal et al., WSDM’2009) • Netflix tutorial at RecSys’2012: diversity is key @Netflix - 22 - Bangalore/MumbaiConfidential Yahoo! 2013
  24. 24. Social Networks and RecommendationComputation • Some are hailing social networks as a silver bullet for recommender systems – Tell me who your friends are and we’ll tell you what you like • Is it really the case that we like the same media as our friends? • Affinity trumps friendship! – There are people out there who are “more like us” than our limited set of friends – Once affinity is considered, the marginal value of social connections is often negligible • Not to be confused with non-friendship social networks, where connections are affinity related (Epinions) - 23 - Bangalore/MumbaiConfidential Yahoo! 2013 RecSys 202
  25. 25. Social Networks and RecommendationConsumption • Previous slide nonewithstanding, “social” is a great motivator for consuming recommendations – People like you rate “Lincoln” very highly vs. – Your friends Alice and Bob saw “Lincoln” last night and loved it • Explaining recommendations for motivating and increasing consumption is an emerging practice • Some commercial systems completely separate their explanation generation from their recommendation generation – So Alice and Bob may not be why the system recommended “Lincoln” to you, but they will be leveraged to get you to watch it • Privacy in the face of joint consumption of a personalized experience? - 24 - Bangalore/MumbaiConfidential Yahoo! 2013 RecSys 202
  26. 26. Questions, Comments? Thank you! rlempel (at) yahoo-inc dot com - 25 - Yahoo! Confidential

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