Modeling User Rating Profiles For Collaborative Filtering

Part of Advances in Neural Information Processing Systems 16 (NIPS 2003)

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Benjamin M. Marlin


In this paper we present a generative latent variable model for rating-based collaborative (cid:12)ltering called the User Rating Pro(cid:12)le model (URP). The generative process which underlies URP is de- signed to produce complete user rating pro(cid:12)les, an assignment of one rating to each item for each user. Our model represents each user as a mixture of user attitudes, and the mixing proportions are distributed according to a Dirichlet random variable. The rating for each item is generated by selecting a user attitude for the item, and then selecting a rating according to the preference pattern associ- ated with that attitude. URP is related to several models including a multinomial mixture model, the aspect model [7], and LDA [1], but has clear advantages over each.