We describe a class of causal, discrete latent variable models called Multiple Multiplicative Factor models (MMFs). A data vector is represented in the latent space as a vector of factors that have discrete, non-negative expression levels. Each factor proposes a distribution over the data vector. The distinguishing feature of MMFs is that they combine the factors' proposed distributions multiplicatively, taking into account factor expression levels. The product formulation of MMFs allow factors to specialize to a subset of the items, while the causal generative semantics mean MMFs can readily accommodate missing data. This makes MMFs distinct from both directed models with mixture semantics and undirected product models. In this paper we present empirical results from the collaborative filtering domain showing that a binary/multinomial MMF model matches the performance of the best existing models while learning an interesting latent space description of the users.

Collaborative filtering (CF) allows the preferences of multiple users to be pooled to make recommendations regarding unseen products. We consider in this paper the problem of online and interactive CF: given the current ratings associated with a user, what queries (new ratings) would most improve the quality of the recommendations made? We cast this terms of expected value of information (EVOI); but the online computational cost of computing optimal queries is prohibitive. We show how offline prototyping and computation of bounds on EVOI can be used to dramatically reduce the required online computation. The framework we develop is general, but we focus on derivations and empirical study in the specific case of the multiple-cause vector quantization model.

In this paper we present a generative latent variable model for rating-based collaborative filtering called the User Rating Profile model (URP). The generative process which underlies URP is designed to produce complete user rating profiles, 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 associated with that attitude. URP is related to several models including a multinomial mixture model, the aspect model, and LDA, but has clear advantages over each.

Guibas conjectured that given a convex polygon P in the xy-plane along with two triangulations of it, T1 and T2 that share no diagonals, it is always possible to assign height values to the vertices of P such that P U T1 U T 2 becomes a convex 3-polytope. Dekster found a counter example but left open the questions of deciding if a given configuration corresponds to a convex 3-polytope, and constructing such realizations when they exist. This paper gives a proof that a relaxed version of Guibas' conjecture always holds true. The question of deciding the realizability of Guibas' conjecture is characterized in terms of a linear programming problem. This leads to an algorithm for deciding and constructing such realizations that incorporates a linear programming step with O(n) inequality constraints and n variables.