A Unified Algorithm for One-Cass Structured Matrix Factorization with Side Information
Abstract
In many applications such as recommender systems and multi-label learning the task is to complete a partially observed binary matrix. Such PU learning (positive-unlabeled) problems can be solved by one-class matrix factorization (MF). In practice side information such as user or item features in recommender systems are often available besides the observed positive user-item connections. In this work we consider a generalization of one-class MF so that two types of side information are incorporated and a general convex loss function can be used. The resulting optimization problem is very challenging, but we derive an efficient and effective alternating minimization procedure. Experiments on large-scale multi-label learning and one-class recommender systems demonstrate the effectiveness of our proposed approach.
Cite
Text
Yu et al. "A Unified Algorithm for One-Cass Structured Matrix Factorization with Side Information." AAAI Conference on Artificial Intelligence, 2017. doi:10.1609/AAAI.V31I1.10863Markdown
[Yu et al. "A Unified Algorithm for One-Cass Structured Matrix Factorization with Side Information." AAAI Conference on Artificial Intelligence, 2017.](https://mlanthology.org/aaai/2017/yu2017aaai-unified/) doi:10.1609/AAAI.V31I1.10863BibTeX
@inproceedings{yu2017aaai-unified,
title = {{A Unified Algorithm for One-Cass Structured Matrix Factorization with Side Information}},
author = {Yu, Hsiang-Fu and Huang, Hsin-Yuan and Dhillon, Inderjit S. and Lin, Chih-Jen},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2017},
pages = {2845-2851},
doi = {10.1609/AAAI.V31I1.10863},
url = {https://mlanthology.org/aaai/2017/yu2017aaai-unified/}
}