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Topic 12 – Recommender Systems and the Netflix Prize

Topic 12 – Recommender Systems and the Netflix Prize. Outline. Intro to Recommender Systems The Netflix Prize – structure and history Models for Recommendation Extensions of model for other domains. Recommender Systems.

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Topic 12 – Recommender Systems and the Netflix Prize

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  1. Topic 12 – Recommender Systems and the Netflix Prize Data Mining - Volinsky - 2011 - Columbia University

  2. Outline • Intro to Recommender Systems • The Netflix Prize – structure and history • Models for Recommendation • Extensions of model for other domains Data Mining - Volinsky - 2011 - Columbia University

  3. Recommender Systems • Systems which take user preferences about items as input and outputs recommendations • Early examples • Bellcore Music Recommender (1995) • MIT Media Lab: Firefly (1996) Best example: Amazon.com Worst example: Amazon.com Also: Netflix eBay Google Reader iTunes Genius digg.com Hulu.com Data Mining - Volinsky - 2011 - Columbia University

  4. Recommender Systems • Basic idea • recommend item i to user u for the purpose of • Exposing them to something they would not have otherwise seen • Leading customers to the Long Tail • Increasing customers’ satisfaction • Data for recommender systems (need to know who likes what) • Purchase/rented • Ratings • Web page views • Which do you think is best? Data Mining - Volinsky - 2011 - Columbia University

  5. Recommender Systems • Two types of data: • Explicit data: user provides information about their preferences • Pro: high quality ratings • Con: Hard to get: people cannot be bothered • Implicit data: infer whether or not user likes product based on behavior • Pro: Much more data available, less invasive • Con: Inference often wrong (does purchase imply preference?) • In either case, data is just a big matrix • Users x items • Entries binary or real-valued • Biggest Problem: • Sparsity: most users have not rated most products. Data Mining - Volinsky - 2011 - Columbia University

  6. Recommender Systems: Models Two camps on how to make recommendations: • Collaborative Filtering (CF) • Use collective intelligence from all available rating information to make predictions for individuals • Depends on the fact that user tastes are correlated and commutative: • If Alice and Bob both like X and Alice likes Y then Bob is more likely to like Y • Content based • Extracts “features” from items for a big regression or rule-based model • See www.nanocrowd .com • 15 years of research in the field • Conventional wisdom: • CF performs better when there is sufficient data • Content-based is useful when there is little data Data Mining - Volinsky - 2011 - Columbia University

  7. Recommender Systems: Evaluation • Narrow focus on accuracy sometimes misses the point • Prediction Diversity, Prediction Context, Order of predictions • For explicit ratings: use training – test setup and use some form of MSE score • OK, but treats all ratings the same (care most about top k) • Doesn’t reflect interventional impact of recommender • For implicit, we don’t have a score to calculate MSE. • Can still do training test • Create a ranked vector of preference – really only interested in top-k for a recommendation algorithm • use “discounted cumulative gain” • Gives more weight to those predicted at the top Data Mining - Volinsky - 2011 - Columbia University

  8. Evaluation: Discounted Cumulative Gain • Recommending stories on a web site: • Ten stories to recommend in test set: ABCDEFGHIJ • Only room on page for 5 recommendations • Algorithm 1 ranks: AECHI • Algorithm 2 ranks: CDBFG • User actually viewed AEBFGJ • rel = {0,1} • DCG1= 1+1/log2(2)+0+0+0 = 2 • DCG2=0+0+1/log2(3)+1/log2(4)+1/log2(5) = 1.56 Data Mining - Volinsky - 2011 - Columbia University

  9. The Netflix Prize Data Mining - Volinsky - 2011 - Columbia University

  10. Netflix • A US-based DVD rental-by mail company • >10M customers, 100K titles, ships 1.9M DVDs per day Good recommendations = happy customers Data Mining - Volinsky - 2011 - Columbia University

  11. Netflix Prize • October, 2006: • Offers $1,000,000 for an improved recommender algorithm • Training data • 100 million ratings • 480,000 users • 17,770 movies • 6 years of data: 2000-2005 • Test data • Last few ratings of each user (2.8 million) • Evaluation via RMSE: root mean squared error • Netflix Cinematch RMSE: 0.9514 • Competition • $1 million grand prize for 10% improvement • If 10% not met, $50,000 annual “Progress Prize” for best improvement Data Mining - Volinsky - 2011 - Columbia University

  12. Netflix Prize • Competition design • Hold-out set created by taking last 9 ratings for each user • Non-random, biased set • Hold-out set split randomly three ways: • Probe Set – appended to training data to allow unbiased estimation of RMSE • Submit ratings for the (Quiz+Test) Sets • Netflix returns RMSE on the Quiz Set only • Quiz Set results posted on public leaderboard, but Test Set used to determine the winner! • Prevents overfitting Data Mining - Volinsky - 2011 - Columbia University

  13. Data Characteristics Data Mining - Volinsky - 2011 - Columbia University

  14. Ratings per movie/user Avg #ratings/movie: 5627 Avg #ratings/user: 208 Data Mining - Volinsky - 2011 - Columbia University

  15. Data Characteristics • Most Loved Movies Data Mining - Volinsky - 2011 - Columbia University

  16. The competition progresses… • Cinematch beaten in two weeks • Halfway to 10% in 6 weeks • Our team, BellKor (Bob Bell and Yehuda Koren) took over the lead in the summer… • With 48 hours to go to the $50K Progress Prize, we had a comfortable lead…with another submission in pocket Data Mining - Volinsky - 2011 - Columbia University

  17. 8pm 6am 10/1 8pm Leaderboard 05:00 pm Sept 30 Data Mining - Volinsky - 2011 - Columbia University

  18. Leaderboard 06:00 pm Sept 30 wanna split 50/50? Data Mining - Volinsky - 2011 - Columbia University

  19. 8pm 6am 10/1 8pm • ARRRRGH! We have one more chance…. Data Mining - Volinsky - 2011 - Columbia University

  20. A Nervous Last Day • Start in virtual tie on Quiz data • Unclear who leads on Test data • Can Gravity/Dinosaurs improve again? • More offers for combining • Can we squeeze out a few more points? • Improved our mixing strategy • Created a second team, KorBell, just to be safe Data Mining - Volinsky - 2011 - Columbia University

  21. 8pm 6am 10/1 8pm Our final submission(s)… Data Mining - Volinsky - 2011 - Columbia University

  22. Data Mining - Volinsky - 2011 - Columbia University

  23. So The Drama Timeline: • 2008: • BellKor merges with BigChaos to win 2nd $50K Progress Prize (9.4%) • 2009: • June 26: BellKor, Big Chaos and Pragmatic Theory merge passing 10% threshold, begins 30 day ‘last call’ period. • July 24: All is quiet. We crawl up to 10.08%... • July 25: The Ensemble, a coalition of 23 independent teams, combine to pass us on the public leaderboard. • July 26: Bellkor pulls back into a tie. Then Ensemble pulls ahead by 0.01 % • July 26: Netflix Prize ends: winner (determined on Test Set) is unclear. • September 21: Netflix announces BellKor’s Pragmatic Chaos as winner of $1M prize. Data Mining - Volinsky - 2011 - Columbia University

  24. So The Drama Timeline: • 2009: • June 26: BellKor, Big Chaos and Pragmatic Theory merge passing 10% threshold, begins 30 day ‘last call’ period. • Lead next best team by 0.37% • Only two teams within 0.80% • But soon, the cavalry charges • Mega-mergers start to form. • July 25 (25 hours left): • We crawl up to 10.08%... • The Ensemble, a coalition of 23 independent teams, combine to pass us on the public leaderboard Data Mining - Volinsky - 2011 - Columbia University

  25. Data Mining - Volinsky - 2011 - Columbia University

  26. Test Set Results • BellKor’s Pragmatic Theory: 0.8567 • The Ensemble: 0.8567 • Tie breaker was submission date/time • We won by 20 minutes! But really: • BellKor’s Pragmatic Theory: 0.856704 • The Ensemble: 0.856714 • Also, a combination of BPC (10.06%) and Ensemble (10.06%) scores results in a 10.19% improvement! Data Mining - Volinsky - 2011 - Columbia University

  27. Our Approach • Our Prize winning solutions were an ensemble of many separate solution sets • Progress Prize 2007: 103 sets • Progress Prize 2008 (w/Big Chaos): 205 sets • Grand Prize 2009 (w/ BC and Pragmatic Theory): > 800 sets!! • We used two main classes of models • Nearest Neighbors • Latent Factor Models (via Singular Value Decomposition) • Also regularized regression, not a big factor • Teammates used neural nets and other methods • Approaches mainly algorithmic, not statistical in nature Data Mining - Volinsky - 2011 - Columbia University

  28. - unknown rating - rating between 1 to 5 Data representation (excluding dates) users movies Data Mining - Volinsky - 2011 - Columbia University

  29. Nearest Neighbors Data Mining - Volinsky - 2011 - Columbia University

  30. - unknown rating - rating between 1 to 5 Nearest Neighbors users movies Data Mining - Volinsky - 2011 - Columbia University

  31. Nearest Neighbors users movies - estimate rating of movie 1 by user 5 Data Mining - Volinsky - 2011 - Columbia University

  32. Nearest Neighbors users movies Neighbor selection:Identify movies similar to 1, rated by user 5 Data Mining - Volinsky - 2011 - Columbia University

  33. Nearest Neighbors users movies Compute similarity weights:s13=0.2, s16=0.3 Data Mining - Volinsky - 2011 - Columbia University

  34. Nearest Neighbors users movies Predict by taking weighted average:(0.2*2+0.3*3)/(0.2+0.3)=2.6 Data Mining - Volinsky - 2011 - Columbia University

  35. Nearest Neighbors • To predict the rating for user u on item i: • Use similar users’ ratings for similar movies: rui= rating for user u and item i bui= baseline rating for user u and item I sij= similarity between items i and j N(i,u) = neighborhood of item i for user u (might be fixed at k) Data Mining - Volinsky - 2011 - Columbia University

  36. Nearest Neighbors • Useful to “center” the data, and model residuals • What is sij ??? • Cosine distance • Correlation • What is N(i,u)?? • Top-k • Threshold • What is bui • How to deal with missing values? • Choose several different options and throw them in! Data Mining - Volinsky - 2011 - Columbia University

  37. Nearest Neighbors, cont • This is called “item-item” NN • Can also do user-user • Which do you think is better? • Advantages of NN • Few modeling assumptions • Easy to explain to users • Most popular RS tool Data Mining - Volinsky - 2011 - Columbia University

  38. Nearest Neighbors, Modified • Problem with traditional k-NN: • Similarity weights are calculated globally, and • do not account for correlation among the neighbors • We estimate the weights (wij) simultaneously via a least squares optimization : Basically, a regression using the ratings in the nbhd. • Shrinkage helps address correlation • (don’t try this at home) Data Mining - Volinsky - 2011 - Columbia University

  39. serious Geared towards males Geared towards females escapist Latent factor models – Singular Value Decomposition SVD finds concepts Braveheart The Color Purple Amadeus Lethal Weapon Sense and Sensibility Ocean’s 11 The Lion King Independence Day The Princess Diaries Dumb and Dumber Data Mining - Volinsky - 2011 - Columbia University

  40. Matrix Decomposition - SVD users Example with 3 factors (concepts ? ~ items users D3 ~ items • Each user and each item is described by a feature vector across concepts Data Mining - Volinsky - 2011 - Columbia University

  41. Factorization-based modeling • This is a strange way to use SVD! • Usually for reducing dimensionality, here for filling in missing data! • Special techniques to do SVD w/ missing data • Alternating Least Squares = variant of EM algorithms • Probably most popular model among contestants • 12/11/2006: Simon Funk describes an SVD based method ~ Data Mining - Volinsky - 2011 - Columbia University

  42. Latent Factor Models, Modified • Problem with traditional SVD: • User and item factors are determined globally • Each user described as a fixed linear combination across factors • What if there are different people in the household? • Let the linear combination change as a function of the item rated. • Substitute pu with pu(i), and add similarity weights • Again, don’t try this at home! Data Mining - Volinsky - 2011 - Columbia University

  43. First 2 Singular Vectors Data Mining - Volinsky - 2011 - Columbia University

  44. Incorporating Implicit Data • Implicit Data: what you choose to rate is an important, and separate piece of information than how you rate it. • Helps incorporate negative information, especially for those users with low variance. • Can be fit in NN or SVD Data Mining - Volinsky - 2011 - Columbia University

  45. Temporal Effects It took us 1.5 years to figure out how to use the dates! Account for temporal effects in two ways • Directly in the SVD model – allow user factor vectors pu to vary with time pu(t) • Many ways to do this, adds many parameters, need regularization • DTTAH • Observed: number of user ratings on a given date is a proxy for how long ago the movie was seen: • Some movies age better than others Data Mining - Volinsky - 2011 - Columbia University

  46. Memento vs Patch Adams rating frequency rating frequency Data Mining - Volinsky - 2011 - Columbia University

  47. Netflix Prize: Other Topics • Ensembles of models • Application to TV data Data Mining - Volinsky - 2011 - Columbia University

  48. The Power of Ensembles • Ensembles of models • Over 800 in Grand Prize solution! • Some of our “models” were blends of other models! • or models on residuals of other models • Why: because it worked • Black Box magic at its worst • However, mega blends are not needed in practice • Best model: complex model with implicit and explicit data, and time varying coefficients, millions of parameters and regularization. • This did as well as our 2007 Progress Prize winning combo of 107 models! • Even a small handful of ‘simple’ models gets you 80-85% of the way • In practice, .01% does not matter much Courtesy LingPipe Blog Data Mining - Volinsky - 2011 - Columbia University

  49. Blending Models • Figuring out the right way to blend models was an important piece of our team strategy • Subsequent slides courtesy of Michael Jahrer (BigChaos)… • Keep in mind… Data Mining - Volinsky - 2011 - Columbia University

  50. Probe Blending < Quiz Blending • A few facts before we go on: • Every model I can be reduced to its prediction vector on the probe set (pi). That can be compared to the true probe answers (r) • The model can also be applied to the quiz set (qi), but can only be compared to the true answers (s) by submission • We submitted all individual models to get the RMSE for every model i. • The distribution of the quiz vector (how many 1s, 2s, 3s, etc) was well known. This gives the variance of the vector Data Mining - Volinsky - 2011 - Columbia University

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