Knowledge-aware Coupled Graph Neural Network for Social Recommendation

Authors

  • Chao Huang JD Finance America Corporation, USA
  • Huance Xu South China University of Technology, China
  • Yong Xu South China University of Technology, China Peng Cheng Laboratory, China Communication and Computer Network Laboratory of Guangdong, China
  • Peng Dai JD Finance America Corporation, USA
  • Lianghao Xia South China University of Technology, China
  • Mengyin Lu JD Finance America Corporation, USA
  • Liefeng Bo JD Finance America Corporation, USA
  • Hao Xing VIPS Research, China
  • Xiaoping Lai VIPS Research, China
  • Yanfang Ye Case Western Reserve University, USA

Keywords:

Recommender Systems & Collaborative Filtering, Graph Mining, Social Network Analysis & Community

Abstract

Social recommendation task aims to predict users' preferences over items with the incorporation of social connections among users, so as to alleviate the sparse issue of collaborative filtering. While many recent efforts show the effectiveness of neural network-based social recommender systems, several important challenges have not been well addressed yet: (i) The majority of models only consider users’ social connections, while ignoring the inter-dependent knowledge across items; (ii) Most of existing solutions are designed for singular type of user-item interactions, making them infeasible to capture the interaction heterogeneity; (iii) The dynamic nature of user-item interactions has been less explored in many social-aware recommendation techniques. To tackle the above challenges, this work proposes a Knowledge-aware Coupled Graph Neural Network (KCGN) that jointly injects the inter-dependent knowledge across items and users into the recommendation framework. KCGN enables the high-order user- and item-wise relation encoding by exploiting the mutual information for global graph structure awareness. Additionally, we further augment KCGN with the capability of capturing dynamic multi-typed user-item interactive patterns. Experimental studies on real-world datasets show the effectiveness of our method against many strong baselines in a variety of settings. Source codes are available at: https://github.com/xhcdream/KCGN.

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Published

2021-05-18

How to Cite

Huang, C., Xu, H., Xu, Y., Dai, P., Xia, L., Lu, M., Bo, L., Xing, H., Lai, X., & Ye, Y. (2021). Knowledge-aware Coupled Graph Neural Network for Social Recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, 35(5), 4115-4122. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16533

Issue

Section

AAAI Technical Track on Data Mining and Knowledge Management