Lecture 14: Collaborative Filtering Based on Breese, J., Heckerman, D., and Kadie, C. (1998). Empirical analysis of predictive algorithms for collaborative.

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Presentation transcript:

Lecture 14: Collaborative Filtering Based on Breese, J., Heckerman, D., and Kadie, C. (1998). Empirical analysis of predictive algorithms for collaborative filtering. Proc. of the 14th Conference on Uncertainty in Artificial Intelligence. COMP 538 Introduction of Bayesian networks

CF Algorithms: N. L. Zhang/HKUST Slide 2 Collaborative Filtering (CF) l Definition: n The task to predict the utility of items to the active user n Based on database of user votes. l Types of algorithms: n Memory-based: operate over the entire user database. n Model-based: build model using user database and use model fo predication.

CF Algorithms: N. L. Zhang/HKUST Slide 3 Basic Issues l Implicit versus explicit votes n Explicit voting: user consciously expressing his or her preference. Not universally practical because people are lazy. n Implicit voting: Based data browsed, items purchased, general access patterns. l Cannot assume missing at random. n Vote  positive vote n Missing vote  negative vote

CF Algorithms: N. L. Zhang/HKUST Slide 4 Memory-Based Algorithm l User database n --- vote by user i on item j. n Mean vote for user i: n Predicted vote of the active user for item j: –n --- number of users in database –W(a, i) --- distance between active user and user i. –K --- normalization constant so that votes sum to 1.

CF Algorithms: N. L. Zhang/HKUST Slide 5 Memory-Based Algorithms l What should w(a, i) be? n Correlation: based on items both a and i have accessed n Vector similarity

CF Algorithms: N. L. Zhang/HKUST Slide 6 Model-Based Methods l Probabilistic view of collaborative filtering –Possible votes: 0, 1, …, m l Need a model, which is to be constructed from user database.

CF Algorithms: N. L. Zhang/HKUST Slide 7 Cluster Model l X: the class variable l Y j : vote on item j. l Use EM to learn parameters l Use approximate marginal likelihood (e.g. BIC, Cheeseman- Stutz) to choose the the number of classes X Y1Y1 Y2Y2 YpYp

CF Algorithms: N. L. Zhang/HKUST Slide 8 Bayesian Network Model l For user database learn Bayesian network where n One node for each item –States of a node are votes. Special state for “no-vote”. n Predict vote on a item based votes on its parents

CF Algorithms: N. L. Zhang/HKUST Slide 9 Evaluation l Training data and testing data n Criteria different for recommending one or a list l Datasets: MS web, Television, EachMovie. l Proctols: all but one, given 2, 5, or 10 l Bayesian network and correlation methods outperform vector similarity and cluster models.