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.
Cite
Text
Xu et al. "Nonparametric Max-Margin Matrix Factorization for Collaborative Prediction." Neural Information Processing Systems, 2012.Markdown
[Xu et al. "Nonparametric Max-Margin Matrix Factorization for Collaborative Prediction." Neural Information Processing Systems, 2012.](https://mlanthology.org/neurips/2012/xu2012neurips-nonparametric/)BibTeX
@inproceedings{xu2012neurips-nonparametric,
title = {{Nonparametric Max-Margin Matrix Factorization for Collaborative Prediction}},
author = {Xu, Minjie and Zhu, Jun and Zhang, Bo},
booktitle = {Neural Information Processing Systems},
year = {2012},
pages = {64-72},
url = {https://mlanthology.org/neurips/2012/xu2012neurips-nonparametric/}
}