文献类型：期刊文献

英文题名：Attentive autoencoder matrix factorization for recommender systems

作者：Zang, Dawei[1]; Guo, Weibin[1]; Li, Zongyin[2]; Liu, Yu[1]

机构：[1] Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China; [2] Institute of Science and Technology Development, East China University of Science and Technology, Shanghai, 200237, China

年份：2018

起止页码：771

外文期刊名：Proceedings of 2018 IEEE 4th Information Technology and Mechatronics Engineering Conference, ITOEC 2018

收录：EI(收录号：20192807156842)

语种：英文

外文关键词：Gaussian noise (electronic) - Matrix factorization - Learning systems - Matrix algebra - Neural networks - User profile

摘要：In real applications, sparseness of user-item rating data significantly causes degrading in recommendation performance. There are several effective techniques which reduce prediction error by auxiliary information. However, these works focus on how to increase the ability of extracting features from a particular part rather than how to fuse features from different parts effectively. And the difference of Gaussian noise is ignored in modeling latent factors of both users and items. In this paper, to address these issues, we propose attentive autoencoder(AAE) to effectively extract features from several different information sources, which is integrated into improved probabilistic matrix factorization (PMF). The improved PMF consider the difference of Gaussian noise between both users and items. We perform extensive experiments on three real-world datasets, and the result shows that our AAEMF significantly outperforms the state-of-the-art models. ? 2018 IEEE.