Recommender systems in practice
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Hands-on data science meetup, 8 December 2017 https://www.meetup.com/Hands-on-Data-Science/events/244078052/
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Recommender systems in practice
- 4. WHAT IS A RECOMMENDER? Predictive model Information filtering system
- 5. SYSTEM DESIGN item user impression preference rating (implicit/explicit) data collaborative content / attributes modelpredictionsrecommendations attributes
- 6. • Goal • Context • Personalization • Data • Algorithm SYSTEM REQUIREMENTS
- 8. CONTENT-BASED FILTERING Depends on item attributes
- 11. Collaborative filtering with Matrix Factorization COLLABORATIVE FILTERING WITH MATRIX FACTORIZATION • User-based and item-based • Customized for implicit feedback • Scalable computation
- 12. BASIC SYSTEM PREPROCESSING MODEL item rankings POSTPROCESSING 1 0 0 1 1 1 1 0 1 user-item interactions user-item ratings 1.2 0.8 0.2 1.3 1.2 1.1 1.2 1.1 0.9 predicted ratings �𝒙𝒙𝑢𝑢,𝑖𝑖 = 𝑓𝑓 𝑢𝑢, 𝑖𝑖 𝒙𝒙𝑢𝑢,𝑖𝑖 = � 1 𝑟𝑟𝑢𝑢,𝑖𝑖 > 0 0 𝑟𝑟𝑢𝑢,𝑖𝑖 = 0 𝒄𝒄𝑢𝑢,𝑖𝑖 = 1 + 𝛼𝛼𝑟𝑟𝑢𝑢,𝑖𝑖
- 13. MATRIX FACTORIZATION 15 = 3 × 5 How does it work?
- 14. MATRIX FACTORIZATION Summarize one large matrix into two smaller (lower-rank) matrices How does it work? 3 4 5 6 8 10 = 1 2 3 4 5
- 15. MATRIX FACTORIZATION items users ratings 𝑿𝑿 × item profiles 𝑯𝑯 userprofiles 𝑾𝑾 ≈ �𝑿𝑿 = predicted ratings
- 16. MATRIX FACTORIZATION predicted ratings= 𝒘𝒘𝑢𝑢 𝒉𝒉𝑖𝑖 = 𝒘𝒘𝑢𝑢 Τ � 𝒉𝒉𝑖𝑖 Predicted rating of user u for item i items �𝒙𝒙𝑢𝑢,𝑖𝑖 users ratings × 𝑿𝑿 ≈
- 17. MATRIX FACTORIZATION 𝑚𝑚𝑚𝑚𝑚𝑚 � 𝑢𝑢,𝑖𝑖 𝒄𝒄𝑢𝑢𝑢𝑢 𝒙𝒙𝑢𝑢𝑢𝑢 − 𝒘𝒘𝑢𝑢 Τ 𝒉𝒉𝑖𝑖 2 + 𝜆𝜆 � 𝑢𝑢 𝒘𝒘𝑢𝑢 2 + � 𝑖𝑖 𝒉𝒉𝑖𝑖 2 weighted prediction error regularization penalty true rating predicted ratingsample weight items users ratings × userprofiles item profiles 𝑾𝑾𝑿𝑿 𝑯𝑯 ≈ Alternating Least Squares 0. Initiate 𝑾𝑾 and 𝑯𝑯 at random 1. Solve alternating: • Fix 𝑯𝑯, optimize 𝑾𝑾 • Fix 𝑾𝑾 , optimize 𝑯𝑯 2. Repeat until convergence
- 18. MATRIX FACTORIZATION item profiles × item-item similarities = • Recommend item-to-item • Cosine similarity of item profiles 𝑺𝑺𝑖𝑖,𝑗𝑗 = 𝒉𝒉𝑖𝑖 � 𝒉𝒉𝑗𝑗 𝒉𝒉𝑖𝑖 2 𝒉𝒉𝑗𝑗 2 𝒔𝒔𝑖𝑖
- 19. THE ESSENCE Using data-driven user and item profiles…. user profiles item profiles …to predict the preference of a specific user for a specific item ratings predicted ratings
- 20. ADVANGATES AND DISADVANTAGES + Simultaneous latent user and item factors + Can handle sparse data + Scalable computation − Temporal and popularity biases − Cold start problem − No context-awareness popularity sorted items TAIL HEAD
- 21. CROSS-VALIDATION train train train test Temporal splitQuasi-random split train test items users Note: test user and item need to be present in train set
- 22. 0.750.25 OFFLINE EVALUATION Ranking metrics Top items more important? (MRR, MAP, nDCG) Simple: Average Percentile Rank TESTTRAIN APR =
- 23. ONLINE EVALUATION Many assumptions and biases in offline recommender evaluation Best option: go live, measure business value
- 24. CONTEXT-AWARE RECOMMENDERS Neural network view of matrix factorization contextmetadatastandard matrix factorization factorization layer … … sparse features output layer …× × … … … dense features / embeddings ×biases +
- 25. PRACTICAL ADVISE • Never instantiate full user-item matrix! • Based on volumes, go for scalable framework (e.g. Spark MLlib) • Based on requirements, go for flexible framework (e.g. TensorFlow)
- 26. SUMMARY • Recommender use cases • Types of recommender algorithms • Matrix factorization • Recommender evaluation • Various challenges >> Time for hands-on!