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A cognitive psychologist's approach to data mining
- 1. A Cognitive Psychologist's- Approach to Data Mining - How I beat Netflix Cinematch Maggie Xiong April 22, 2014
- 2. Parallel Frameworks Cognitive psychology & data mining Case Study The Netflix Prize Project General Outline
- 3. Abstraction and Generalization Categorization Prototype Exemplar Decision boundary Theory-based categories Semantic space / LSA Connectionism
- 4. Abstraction Linguistic ideas (Bransford & Franks, 1971) “The ants in the kitchen ate the sweet jelly which was on the table.” “The ants in the kitchen ate the sweet jelly.” “The ants in the kitchen ate the jelly.” “The ants were in the kitchen.” Participants were more confident in “recognizing” fuller sentences. Prototype (Posner & Keele, 1968) Participants studied instances generated from distortions of prototypes. They showed the same accuracy and response time for never-seen prototypes and memorized instances in a later test.
- 5. Categorization Category structure (Collins & Quillian, 1969) Economy of organization Participants takes longer to respond to statements across category levels. Typicality Exemplar (Jacoby & Brooks, 1984)
- 6. Decision Boundary Theory-based Categories Decision boundary (Ashby & Gott, 1988) Theory-based categories (Murphy & Medin, 1985) Categories organized around theories about the world. clean vs unclean foods; apples and prime numbers
- 7. Semantic Space Latent Semantic Analysis Shepard, 1987 Probability of generalization decays exponentially with distance. Osgood, 1957 Factor analysis Evaluative, potency, activity Dumais et al., 1988 SVD, cosine similarity Landauer & Dumais, 1997
- 8. Semantic Space Latent Semantic Analysis Shepard, 1987 Probability of generalization decays exponentially with distance. Osgood, 1957 Factor analysis Evaluative, potency, activity Dumais et al., 1988 SVD, cosine similarity Landauer & Dumais, 1997
- 9. Connectionism Selfridge, 1958 Pandemonium Rumelhart, McClelland, & PDP Research Group, 1986 Parallel Distributed Processing, 2 Vol Set
- 10. Connectionism Rumelhart & Todd, 1993
- 11. Common Ground Prototype Kmeans Exemplar K-Nearest Neighbor Theory-based categories Collaborative filtering, decision-tree Decision boundary Support Vector Machine Semantic space / LSA Connectionism - artificial neural net
- 12. How Cognitive Psychologists Analyze Data Task completion rate: Main effect of coffee avg(10,8,10,23,18,15) - avg(12,13,10,14,15,12) Main effect of time-of-day avg(14,15,12,23,18,15) - avg(12,13,10,10,8,10) Interaction [avg(23,18,15) - avg(14,15,12)] - [avg(10,8,10) - avg(12,13,10)] 1 Cup 3 Cups Morning 12,13,10 10,8,10 Evening 14,15,12 23,18,15
- 13. Graph It Main effects and interaction Rate Evening Morning Cups of Coffee
- 14. Training set 17770 movies, 500K users, 100M ratings user_id, movie_id, rating, date_of_rating movie_id, title, year Probe set (1.4M ratings) Qualifying set (2.8M ratings) user_id, movie_id, date_of_rating RMSE sqrt( sum(X - X.pred)2 / N ) Cinematch: 0.9514 The Netflix Prize Problem, 2006/10/02 0.8563 => $1M
- 15. Standard Deviation and RMSE
- 16. The Netflix Problem, Interpreted Overall average movie rating: 3.620* Main effect of movie: Miss Congeniality: avg(u1,u2,u3...) Mission Impossible: avg(u1,u2,u3...) Main effect of user: Alex: avg(m1,m2,m3…) Brian: avg(m2,m2,m3…) Interaction: Alex - Miss Congeniality, Mission Impossible, ... Brian - Miss Congeniality, Mission Impossible, ...
- 17. RMSE, Appreciated Overall standard deviation: 1.0822* “Trivial approach” (main effect of movie): 1.0540 Main effects of movie and user: 0.9889* R.pred = M.avg + U.dev Cinematch: 0.9514 ... ... Prize: 0.8563
- 18. The Arithmetic Approach R = M.avg + U.dev + interaction interaction = R - (M.avg + U.dev) R.pred = M.avg + U.dev + w.avg(interaction * sim(M.p, M)) Alex R M.avg dev interaction Mission Impossible 4 4.3 -0.3 4 - [4.3 + (-1.4)] = 1.1 Coyote Ugly 1 3.5 -2.5 1 - [3.5 + (-1.4)] = - 1.1 Miss Congeniality ? 4.5 Alex U.dev = ((4 - 4.3) + (1 - 3.5)) / 2 = -1.4 sim(Miss Congeniality, Coyote Ugly) = 0.8 sim(Miss Congeniality, Mission Impossible) = 0.2 ? = 4.5 + (-1.4) + (-1.1*0.8 + 1.1 * 0.2) / (|0.8| + |0.2|) = 2.44
- 19. Similarity Measures Romesburg, 1984 Shape difference vs. Size displacement Euclidean distance Cosine similarity Correlation coefficient
- 20. Movie Similarity Similarity measures Co-occurrence count How often a person rented both movies. Correlation A function of the difference in ratings when a person rented both movies. Correlation weighted by probability (significance) Mean Euclidean distance of movie x user interactions interaction = R - (M.avg + U.dev) Weighted by similarities in movie release times, rental frequencies, mean ratings
- 21. User Clusters Differentiate movie mean rating and similarity R.pred = M.cluster_avg + U.cluster_dev + w.avg(interaction * sim_cluster(M,M.p)) By experience (number of movies rated) [2,180], [81,180], [181,240], [240,400], [401,3000] By gender Inferred from preference for different movie clusters By cluster analysis PCA, Kmeans
- 22. Blend It Generate different sets of predictions using different movie similarity and user cluster strategies Use linear regression to combine the sets of predictions into one final prediction Weak learners are good too, as long as they provide unique information.
- 23. RMSE, 2008/04/01 Overall standard deviation: 1.0822* “Trivial approach” (main effect of movie): 1.0540 Main effects of movie and user: 0.9889* R.pred = M.avg + U.dev Cinematch: 0.9514 ... Naga FX: 0.9063 ... Prize: 0.8563
- 24. Cognitive theories and data mining methods Prototype K-Means Exemplar K-Nearest Neighbor Theory-based categories Collaborative filtering, decision-tree Decision boundary Support Vector Machine Semantic space / LSA Connectionism - artificial neural net Abstraction and generalization It’s all about similarity. Tversky, 1977 Murphy & Medin, 1985 Looking Back