@inproceedings{c720b75cf2fc463ba39fa1577e70cf14,
title = "Make users and preferred items closer: Recommendation via distance metric learning",
abstract = "Recommender systems can help to relieve the dilemma called information overload. Collaborative filtering is a primary approach based on collective historical ratings to recommend items to users. One of the most competitive collaborative filtering algorithm is matrix factorization. In this paper, we proposed an alternative method. It aims to make users be spatially close to items they like and be far away from items they dislike, by connecting matrix factorization and distance metric learning. The metric and latent factors are trained simultaneously and then used to generate reliable recommendations. The experiments conducted on the real-world datasets have shown that, compared with methods only based on factorization, our method has advantage in terms of accuracy.",
keywords = "Collaborative filtering, Distance metric learning, Matrix factorization, Recommendation",
author = "Junliang Yu and Min Gao and Wenge Rong and Yuqi Song and Qianqi Fang and Qingyu Xiong",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 24th International Conference on Neural Information Processing, ICONIP 2017 ; Conference date: 14-11-2017 Through 18-11-2017",
year = "2017",
doi = "10.1007/978-3-319-70139-4\_30",
language = "英语",
isbn = "9783319701387",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "297--305",
editor = "Dongbin Zhao and Yuanqing Li and El-Alfy, \{El-Sayed M.\} and Derong Liu and Shengli Xie",
booktitle = "Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings",
address = "德国",
}