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2004
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The multiple multiplicative factor model for collaborative filtering

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The multiple multiplicative factor model for collaborative filtering
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.
Benjamin M. Marlin, Richard S. Zemel
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2004
Where ICML
Authors Benjamin M. Marlin, Richard S. Zemel
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