Top-N Recommender System via Matrix Completion
Abstract
Top-N recommender systems have been investigated widely both in industry and academia. However, the recommendation quality is far from satisfactory. In this paper, we propose a simple yet promising algorithm. We fill the user-item matrix based on a low-rank assumption and simultaneously keep the original information. To do that, a nonconvex rank relaxation rather than the nuclear norm is adopted to provide a better rank approximation and an efficient optimization strategy is designed. A comprehensive set of experiments on real datasets demonstrates that our method pushes the accuracy of Top-N recommendation to a new level.
Cite
Text
Kang et al. "Top-N Recommender System via Matrix Completion." AAAI Conference on Artificial Intelligence, 2016. doi:10.1609/AAAI.V30I1.9967Markdown
[Kang et al. "Top-N Recommender System via Matrix Completion." AAAI Conference on Artificial Intelligence, 2016.](https://mlanthology.org/aaai/2016/kang2016aaai-top/) doi:10.1609/AAAI.V30I1.9967BibTeX
@inproceedings{kang2016aaai-top,
title = {{Top-N Recommender System via Matrix Completion}},
author = {Kang, Zhao and Peng, Chong and Cheng, Qiang},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2016},
pages = {179-185},
doi = {10.1609/AAAI.V30I1.9967},
url = {https://mlanthology.org/aaai/2016/kang2016aaai-top/}
}