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Multi-head Attentive Social Recommendation

  • Xu LuoEmail author
  • Chaofeng Sha
  • Zijing Tan
  • Junyu Niu
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11881)

Abstract

Recently social relationship among users has been exploited to improve the recommendation performance. The intuition behind most of these work is social homophily such that users are more similar to their neighbors. Attention mechanism or attention network from deep learning has been a popular component employed by recommendation models. However, how to attentively learn the influence between users remains pretty much open in the existing social recommendation models. In this paper, we propose a social recommendation model MAS, Multi-head Attentive Social Recommendation. The key to MAS is a multi-head attention network which can distinguish the impact of users’ friends when predicting users’ preference on different items. When compared to the state-of-the-art baseline methods on three real-world datasets, our method achieves the best performance.

Keywords

Collaborative filtering Social recommendation Attention network 
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Notes

Acknowledgments

This work was supported by National Key R&D Program of China (Grant No. 2018YFB0904503) and the National Natural Science Foundation of China (NFSC) under Grant No. 61572135.

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Computer Science, Shanghai Key Laboratory of Intelligence ProcessingFudan UniversityShanghaiChina

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