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- 1. Emmys, Oscars, and Machine Learning Algorithms @ Netflix Scale November, 2013 Xavier Amatriain Director -‐ Algorithms Engineering -‐ Ne<lix @xamat
- 2. “In a simple Netlfix-style item recommender, we would simply apply some form of matrix factorization (i.e NMF)”
- 3. What we were interested in: § High quality recommendations Proxy question: § Accuracy in predicted rating § Improve by 10% = $1million!
- 4. From the Netflix Prize to today 2006 2013
- 5. Big Data @Netflix § > 40M subscribers § Ratings: ~5M/day Time § Searches: >3M/day Geo-information § Plays: > 50M/day Impressions Device Info § Streamed hours: Metadata Social Ratings o 5B hours in Q3 2013 Member Behavior Demographics
- 6. Smart Models § Regression models (Logistic, Linear, Elastic nets) § SVD & other MF models § Factorization Machines § Restricted Boltzmann Machines § Markov Chains & other graph models § Clustering (from k-means to HDP) § Deep ANN § LDA § Association Rules § GBDT/RF § …
- 7. Deep-dive into Netflix Algorithms
- 8. Rating Prediction
- 9. 2007 Progress Prize ▪ Top 2 algorithms ▪ SVD - Prize RMSE: 0.8914 ▪ RBM - Prize RMSE: 0.8990 ▪ Linear blend Prize RMSE: 0.88 ▪ Currently in use as part of Netflix’ rating prediction component ▪ Limitations ▪ Designed for 100M ratings, we have 5B ratings ▪ Not adaptable as users add ratings ▪ Performance issues
- 10. SVD - Definition A[n x m] = U[n x r] Λ [ r x r] (V[m x r])T ▪ A: n x m matrix (e.g., n documents, m terms) ▪ U: n x r matrix (n documents, r concepts) ▪ ▪ Λ: r x r diagonal matrix (strength of each ‘concept’) (r: rank of the matrix) V: m x r matrix (m terms, r concepts)
- 11. SVD - Properties ▪ ‘spectral decomposition’ of the matrix: = u1 u2 x λ1 λ2 ▪ ‘documents’, ‘terms’ and ‘concepts’: § U: document-to-concept similarity matrix § V: term-to-concept similarity matrix § Λ: its diagonal elements: ‘strength’ of each concept x v1 v2
- 12. SVD for Rating Prediction § User factor vectors § Baseline (bias) and item-factors vectors (user & item deviation from average) § Predict rating as § SVD++ (Koren et. Al) asymmetric variation w. implicit feedback § Where § are three item factor vectors § Users are not parametrized
- 13. Restricted Boltzmann Machines § Restrict the connectivity in ANN to make learning easier. § Only one layer of hidden units. § § Although multiple layers are possible No connections between hidden units. § Hidden units are independent given the visible states.. § RBMs can be stacked to form Deep Belief Networks (DBN) – 4th generation of ANNs
- 14. What about the final prize ensembles? ▪ Our offline studies showed they were too computationally intensive to scale ▪ Expected improvement not worth the engineering effort ▪ Plus, focus had already shifted to other issues that had more impact than rating prediction...
- 15. Ranking Ranking
- 16. Ranking § Ranking = Scoring + Sorting + Filtering bags of movies for presentation to a user § Factors § Accuracy § Novelty Key algorithm, sorts titles in most contexts § Diversity § Freshness § Goal: Find the best possible ordering of a set of videos for a user within a specific context in real-time § Scalability § § Objective: maximize consumption & “enjoyment” § …
- 17. Example: Two features, linear model 2 3 4 5 Popularity Linear Model: frank(u,v) = w1 p(v) + w2 r(u,v) + b Final Ranking Predicted RaEng 1
- 18. Example: Two features, linear model 2 3 4 5 Popularity Final Ranking Predicted RaEng 1
- 19. Ranking
- 20. Learning to Rank ▪ Ranking is a very important problem in many contexts (search, advertising, recommendations) ▪ Quality of ranking is measured using ranking metrics such as NDCG, MRR, MAP, FPC… ▪ It is hard to optimize machine-learned models directly on these measures ▪ They are not differentiable ▪ We would like to use the same measure for optimizing and for the final evaluation
- 21. Learning to Rank Approaches § ML problem: construct ranking model from training data 1. Pointwise (Ordinal regression, Logistic regression, SVM, GBDT, …) Loss function defined on individual relevance judgment § 2. Pairwise (RankSVM, RankBoost, RankNet, FRank…) § § 3. Loss function defined on pair-wise preferences Goal: minimize number of inversions in ranking Listwise § Indirect Loss Function (RankCosine, ListNet…) § Directly optimize IR measures (NDCG, MRR, FCP…) § Genetic Programming or Simulated Annealing § Use boosting to optimize NDCG (Adarank) § Gradient descent on smoothed version (CLiMF, TFMAP, GAPfm @cikm13) § Iterative Coordinate Ascent (Direct Rank @kdd13)
- 22. Similarity
- 23. Similars
- 24. What is similarity? § Similarity can refer to different dimensions § Similar in metadata/tags § Similar in user play behavior § Similar in user rating behavior § … § You can learn a model for each of them and finally learn an ensemble
- 25. Graph-based similarities 0.8 0.3 0.3 0.4 0.2 0.3 0.7
- 26. Example of graph-based similarity: SimRank ▪ SimRank (Jeh & Widom, 02): “two objects are similar if they are referenced by similar objects.”
- 27. Final similarity ensemble § Come up with a score of play similarity, rating similarity, tag-based similarity… § Combine them using an ensemble § Weights are learned using regression over existing response § The final concept of “similarity” responds to what users vote as similar
- 28. Row Selection
- 29. Row SelecEon Inputs § Visitor data § Video history § Queue adds § RaEngs § Taste preferences § Predicted raEngs § PVR ranking § etc. § Global data § Metadata § Popularity § etc.
- 30. Row SelecEon Core Algorithm SelecEon constraints Candidate data Groupify Score/Select ParEal selecEon Group selecEon
- 31. SelecEon Constraints • Many business rules and heurisEcs – RelaEve posiEon of groups – Presence/absence of groups – Deduping rules – Number of groups of a given type • Determined by past A/B tests • Evolved over Eme from a simple template
- 32. GroupiﬁcaEon • Ranking of Etles according to ranking algo • Filtering • Deduping • Forma^ng Candidate data • SelecEon of evidence • ... First n selected groups Group for posiEon n +1
- 33. Scoring and SelecEon • Features considered – ARO scorer – Greedy algorithm – Quality of items – Framework supports other methods – Diversity of tags – Quality of evidence § Backtracking – Freshness § Approximate integer programming – Recency – ... § etc.
- 34. Search Recommendations
- 35. Search Recommendation CombinaEon of Diﬀerent models Play Aaer Search: TransiEon on Play aaer Query Play Related to Search: Similarity between user’s (query, play) … • • • Read this paper for a description of a similar system at LinkedIn
- 36. Unavailable Title Recommendations
- 37. Gamification Algorithms
- 38. rating gameGame Rating Mood Selection Algorithm 1. Editorially selected moods 2. Sort them according to users consumption 3. Take into account freshness, diversity...
- 39. More data or better models?
- 40. More data or better models? Really? Anand Rajaraman: Former Stanford Prof. & Senior VP at Walmart
- 41. More data or better models? Sometimes, it’s not about more data
- 42. More data or better models? [Banko and Brill, 2001] Norvig: “Google does not have better Algorithms, only more Data” Many features/ low-bias models
- 43. More data or better models? Sometimes, it’s not about more data
- 44. “Data without a sound approach = noise”
- 45. Conclusion
- 46. More data + Smarter models + More accurate metrics + Better system architectures Lots of room for improvement!
- 47. Xavier Amatriain (@xamat) xavier@netflix.com Thanks! We’re hiring!

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