CASIA OpenIR  > 多媒体计算与图形学团队
CSAN: Contextual Self-Attention Network for User Sequential Recommendation
Xiaowen Huang1,2; Shengsheng Qian1; Quan Fang1; Jitao Sang3,4; Changsheng Xu1,2
2018-10
Conference NameACM international conference on Multimedia
Conference DateOctober 22-26, 2018
Conference PlaceSeoul, Republic of Korea
Abstract

The sequential recommendation is an important task for online user-oriented services, such as purchasing products, watching videos, and social media consumption. Recent work usually used RNN-based methods to derive an overall embedding of the whole behavior sequence, which fails to discriminate the significance of individual user behaviors and thus decreases the recommendation performance. Besides, RNN-based encoding has fixed size and makes further recommendation application inefficient and inflexible. The online sequential behaviors of a user are generally heterogeneous, polysemous, and dynamically context-dependent. In this paper, we propose a unified Contextual Self-Attention Network (CSAN) to address the three properties. Heterogeneous user behaviors are considered in our model that are projected into a common latent semantic space. Then the output is fed into the feature-wise self-attention network to capture the polysemy of user behaviors. In addition, the forward and backward position encoding matrices are proposed to model dynamic contextual dependency. Through extensive experiments on two real-world datasets, we demonstrate the superior performance of the proposed model compared with other state-of-the-art algorithms.

Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/25826
Collection多媒体计算与图形学团队
Affiliation1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
3.School of Computer and Information Technology & Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University
4.State Key Laboratory for Novel Software Technology, Nanjing University
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Xiaowen Huang,Shengsheng Qian,Quan Fang,et al. CSAN: Contextual Self-Attention Network for User Sequential Recommendation[C],2018.
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