文献类型：会议论文

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]East China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Inst Sci & Technol Dev, Shanghai 200237, Peoples R China

会议论文集：IEEE 4th Information Technology and Mechatronics Engineering Conference (ITOEC)

会议日期：DEC 14-16, 2018

会议地点：Chongqing, PEOPLES R CHINA

语种：英文

外文关键词：Collaborative Filtering; Neural Network; Attention Mechanism

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.