Poster Spotlights Conference on Uncertainty in Artificial Intelligence Catalina Island, United States August 15-17, 2012 Session: Wed. 15 August 2012, 15: :30 pm ID: 211 Response Aware Model-Based Collaborative Filtering Guang Ling 1, Haiqin Yang 1, Michael R. Lyu 1, Irwin King 1,2 1 The Chinese University of Hong Kong 2 AT&T Labs Research, San Francisco

Response Aware Model-Based Collaborative Filtering Motivation  Rating data contain explicit scores and implicit response patterns Rating matrix X User selected items Items Users Data model: probabilistic matrix factorization ( θ=(U, V) )

Response Aware Model-Based Collaborative Filtering Motivation  Rating data contain explicit scores and implicit response patterns  The rated items are not randomly selected Rating matrix X User selected items Randomly selected items Items Users Data model: probabilistic matrix factorization ( θ=(U, V) )

Response Aware Model-Based Collaborative Filtering Motivation  Rating data contain explicit scores and implicit response patterns  The rated items are not randomly selected Goal : How to integrate users’ response patterns into a successful matrix factorization, Probabilistic Matrix Factorization, to avoid bias parameter estimation Rating matrix X User selected items Randomly selected items Items Users Data model: probabilistic matrix factorization ( θ=(U, V) )

Response Aware Model-Based Collaborative Filtering Motivation  Rating data contain explicit scores and implicit response patterns  The rated items are not randomly selected Goal : How to integrate users’ response patterns into a successful matrix factorization, Probabilistic Matrix Factorization, to avoid bias parameter estimation Rating matrix X User selected items Randomly selected items Items Users Items Users Response matrix R Data model: probabilistic matrix factorization ( θ=(U, V) ) Response model: variants of soft assignment of Bernoulli distribution with parameters μ

Response Aware Model-Based Collaborative Filtering Motivation  Rating data contain explicit scores and implicit response patterns  The rated items are not randomly selected Goal : How to integrate users’ response patterns into a successful matrix factorization, Probabilistic Matrix Factorization, to avoid bias parameter estimation Experiments  Three recommender protocols  Synthetic and Yahoo! datasets  RAPMF performs better on randomly selected items Rating matrix X User selected items Randomly selected items Items Users Items Users Response matrix R Synthetic dataset Yahoo! dataset Data model: probabilistic matrix factorization ( θ=(U, V) ) Response model: variants of soft assignment of Bernoulli distribution with parameters μ