Nonparametric Max-Margin Matrix Factorization for Collaborative Prediction

Part of Advances in Neural Information Processing Systems 25 (NIPS 2012)

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Minjie Xu, Jun Zhu, Bo Zhang


We present a probabilistic formulation of max-margin matrix factorization and build accordingly a nonparametric Bayesian model which automatically resolves the unknown number of latent factors. Our work demonstrates a successful example that integrates Bayesian nonparametrics and max-margin learning, which are conventionally two separate paradigms and enjoy complementary advantages. We develop an efcient variational algorithm for posterior inference, and our extensive empirical studies on large-scale MovieLens and EachMovie data sets appear to justify the aforementioned dual advantages.