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Context-aware Recommendation: A Quick View

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Very basic introduction about Context-aware Recommendation.

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Context-aware Recommendation: A Quick View

  1. 1. Context-aware Recommendation: A Quick View Yong Zheng Center for Web Intelligence DePaul University, Chicago Feb 23, 2016
  2. 2. Outline • Background Recommender Systems Evaluation Matrix Factorization • Context-aware Recommendation Context Contextual PreFiltering Contextual Modeling • CARSKit: A Context-aware Recommendation Library 2
  3. 3. Background
  4. 4. Recommender System (RS) • RS: item recommendations tailored to user tastes 4
  5. 5. How it works 5
  6. 6. How it works 6
  7. 7. How it works 7
  8. 8. How it works 8 Binary FeedbackRatings Reviews Behaviors • User Preferences Explicit Implicit
  9. 9. 9 Task and Eval: Rating Prediction User Item Rating U1 T1 4 U1 T2 3 U1 T3 3 U2 T2 4 U2 T3 5 U2 T4 5 U3 T4 4 U1 T4 3 U2 T1 2 U3 T1 3 U3 T2 3 U3 T3 4 Train Test Task: P(U, T) in testing set Assume a simple model: P(U, T) = Avg (T) P(U1, T4) = Avg(T4) = (5+4)/2 = 4.5 P(U2, T1) = Avg(T1) = 4/1 = 4 P(U3, T1) = Avg(T1) = 4/1 = 4 P(U3, T2) = Avg(T2) = (3+4)/2 = 3.5 P(U3, T3) = Avg(T3) = (3+5)/2 = 4 Mean Absolute Error (MAE) = ei = R(U, T) – P(U, T) MAE = (|3 – 4.5| + |2 - 4| + |3 - 4| + |3 – 3.5| + |4 - 4|) / 5 = 1
  10. 10. 10 Task and Eval: Top-N Recommendation User Item Rating U1 T1 4 U1 T2 3 U1 T3 3 U2 T2 4 U2 T3 5 U2 T4 5 U3 T4 4 U1 T4 3 U2 T1 2 U3 T1 3 U3 T2 3 U3 T3 4 Train Test Task: Top-N Items to user U3 Assume a simple model: P(U, T) = Avg (T) P(U3, T1) = Avg(T1) = 4/1 = 4 P(U3, T2) = Avg(T2) = (3+4)/2 = 3.5 P(U3, T3) = Avg(T3) = (3+5)/2 = 4 P(U3, T4) = Avg(T4) = (4+5)/2 = 3.5 Predicted Rank: T3, T1, T4, T2 Real Rank: T3, T2, T1 Precision@N = # of hits/N Precision@1 = 1/1 Precision@2 = 2/2 Precision@3 = 2/3
  11. 11. 11 More Evaluation Metrics • There are many more evaluation metrics Task: Rating Prediction MAE, RMSE, MSE, MPE, etc Task: Top-N Recommendation Relevance: Precision, Recall, F-Measure, AUC, etc Ranking: MAP, NDCG, MRR, etc Business Metrics Retention rate, response rate, purchases, etc
  12. 12. 12 Matrix Factorization (MF) User HarryPotter Batman Spiderman U1 5 3 4 U2 ? 2 4 U3 4 2 ? R P Q
  13. 13. 13 Matrix Factorization (MF) R P Q R = Rating Matrix, m users, n movies; P = User Matrix, m users, f latent factors/features; Q = Item Matrix, n movies, f latent factors/features; Interpretation: pu indicates how much user likes f latent factors; qi means how much one item obtains f latent factors; The dot product indicates how much user likes item;
  14. 14. 14 Matrix Factorization (MF) minq,p S (u,i) e R ( rui - qt i pu )2 + l (|qi|2 + |pu|2 ) Goal: Try to learn P and Q by minimizing the squared error goodness of fit regularization Goodness of fit: to reduce the prediction errors; Regularization term: to alleviate the overfitting;
  15. 15. 15 Matrix Factorization (MF) Optimization using stochastic gradient descent (SGD) Parameter updates based on SGD
  16. 16. 16 MovieLens-100K, http://grouplens.org/datasets/movielens/ 100K ratings given by 943 users on 1,682 movies 0.006 0.007 0.008 0.009 0.01 0.011 0.012 0.013 0.014 0.015 0.016 0.7 0.72 0.74 0.76 0.78 0.8 0.82 ItemAvg ItemKNN MF Precision MAE MAE Precision@10 Matrix Factorization (MF)
  17. 17. Context-aware Recommendation
  18. 18. Non-context vs Context 18 Companion • Decision Making = Rational + Contextual • Examples:  Travel destination: in winter vs in summer  Movie watching: with children vs with partner  Restaurant: quick lunch vs business dinner
  19. 19. What is Context? 19 • “Context is any information that can be used to characterize the situation of an entity” by Anind K. Dey, 2001 • Observed Context: Contexts are those variables which may change when a same activity is performed again and again. • Examples: Watching a movie: time, location, companion, etc Listening to a music: time, location, emotions, occasions, etc
  20. 20. Context-aware RS (CARS) 20 • Traditional RS: Users × Items Ratings • Contextual RS: Users × Items × Contexts Ratings Example of Multi-dimensional Context-aware Data set User Item Rating Time Location Companion U1 T1 3 Weekend Home Kids U1 T2 5 Weekday Home Partner U2 T2 2 Weekend Cinema Partner U2 T3 3 Weekday Cinema Family U1 T3 ? Weekend Cinema Kids
  21. 21. 21 • There are three ways to build algorithms for CARS Context-aware RS (CARS)
  22. 22. Contextual PreFiltering
  23. 23. 23 • List of Contextual PreFiltering Algorithms Reduction-based approach, 2005 Exact and Generalized PreFiltering, 2009 Item Splitting, 2009 User Splitting, 2011 Dimension as Virtual Items, 2011 User-Item Splitting, 2014 Contextual PreFiltering
  24. 24. 24 The underlying idea in item splitting is that the nature of an item, from the user's point of view, may change in different contextual conditions, hence it may be useful to consider it as two different items. (L. Baltrunas, F. Ricci, RecSys'09) – In short, contexts are dependent with items. Contextual PreFiltering (Item Splitting) At Cinema At Home At Swimming Pool
  25. 25. 25 Contextual PreFiltering (Item Splitting) User Item Location Rating U1 M1 Pool 5 U2 M1 Pool 5 U3 M1 Pool 5 U1 M1 Home 2 U4 M1 Home 3 U2 M1 Home 2 High Rating Low Rating Significant difference? Let’s split it !!! M11: being seen at Pool M12: being seen at Home M1 Same movie, different IDs.
  26. 26. 26 Contextual PreFiltering (Item Splitting) User Item Loc Rating U1 M1 Pool 5 U2 M1 Pool 5 U3 M1 Pool 5 U1 M1 Home 2 U4 M1 Home 3 U2 M1 Cinema 2 User Item Rating U1 M11 5 U2 M11 5 U3 M11 5 U1 M12 2 U4 M12 3 U2 M12 2 Transformation If there is qualified split, one item will be split to two new ones. A binary contextual condition for splitting: “Pool” vs. “Non-Pool” After transformation, we obtain a 2D User-Item rating matrix, so that any traditional recommendation algorithms can be applied to.
  27. 27. 27 Contextual PreFiltering (Item Splitting) User Item Loc Rating U1 M1 Pool 5 U2 M1 Pool 5 U3 M1 Pool 5 U1 M1 Home 2 U4 M1 Home 3 U2 M1 Cinema 2 User Item Rating U1 M11 5 U2 M11 5 U3 M11 5 U1 M12 2 U4 M12 3 U2 M12 2 Transformation Question: How to find such a split? Pool and Non-pool, or Home and Non-home? We employ a t-test on two pieces of ratings, the best choice should help obtain the largest t value and a small p-value (e.g., < 0.05)
  28. 28. 28 Contextual PreFiltering (Other Splitting)
  29. 29. 29 Example of Splitting Approaches 0.76 0.78 0.8 0.82 0.84 0.86 0.88 0.9 0.92 0.94 0.96 MF ItemSplitting UserSplitting UISplitting MAE Restaurant data: 2309 ratings given by 50 users on 40 restaurants in context Time and Location
  30. 30. Contextual Modeling
  31. 31. 31 • List of Contextual Modeling Algorithms  Tensor Factorization, 2010  Factorization Machines, 2011  Deviation-Based Context-aware Matrix Factorization, 2011  Deviation-Based Contextual Sparse Linear Method, 2014  Similarity-Based Context-aware Matrix Factorization, 2015  Similarity-Based Contextual Sparse Linear Method, 2015 Contextual Modeling
  32. 32. 32 • Deviation-Based Context-aware MF (CAMF) Contextual Rating Deviation (CRD): how user’s rating is deviated? CRD(1) = 0.5  Users’ rating in Weekday is generally higher than users’ rating at Weekend by 0.5 CRD(2) = -0.1  Users’ rating in Cinema is generally lower than users’ rating at Home by 0.1 Deviation-Based Context-aware MF Context D1: Time D2: Location c1 Weekend Home c2 Weekday Cinema CRD(i) 0.5 -0.1
  33. 33. 33 Deviation-Based Context-aware MF: CAMF_C Deviation-Based Context-aware MF BiasedMF in Traditional RS: CAMF_C Approach: Global Average Rating User bias Item Bias User-Item interaction Contextual Rating Deviation
  34. 34. 34 Deviation-Based Context-aware MF: CAMF_CU & CAMF_CI Deviation-Based Context-aware MF BiasedMF in Traditional RS: CAMF_C Approach: Global Average Rating User bias Item Bias User-Item interaction CAMF_CU Approach: CAMF_CI Approach:
  35. 35. 35 Example: CAMF Restaurant data: 2309 ratings given by 50 users on 40 restaurants in context Time and Location 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1 MF ItemSplitting UserSplitting UISplitting CAMF_C CAMF_CI CAMF_CU MAE
  36. 36. CARSKit: A Library
  37. 37. 37 CARSKit: A Java-based Open-source Context-aware Recommendation Library There are many recommendation library for traditional recommendation. Users × Items Ratings
  38. 38. 38 CARSKit: A Java-based Open-source Context-aware Recommendation Library CARSKit: https://github.com/irecsys/CARSKit Users × Items × Contexts Ratings
  39. 39. Yong Zheng Center for Web Intelligence DePaul University, Chicago Feb 23, 2016 Context-aware Recommendation: A Quick View

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