2012
NeurIPS
NeurIPS 2012
Nonparametric Max-Margin Matrix Factorization for Collaborative Prediction
Abstract
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.
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Interdisciplinary Bridge
- Artificial Intelligence and Data Science & Analytics
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Hot Topic Early Bird
- collaborative filtering
Topics
Artificial Intelligence > Bayesian & Probabilistic > Bayesian Learning
Machine Learning > Core Methods > Representation Learning
Machine Learning > Optimization & Theory > Bayesian Inference
Data Science & Analytics > Applications > Recommender Systems
Machine Learning > Bayesian & Probabilistic > Bayesian Learning
Machine Learning > Core Methods > Matrix Factorization
Machine Learning > Bayesian & Probabilistic > Nonparametric Bayesian
Machine Learning > Learning Types > Collaborative Filtering