Who You Would Like to Share With? A Study of Share Recommendation in Social E-commerce


  • Houye Ji Beijing University of Posts and Telecommunications
  • Junxiong Zhu Alibaba Group
  • Xiao Wang Beijing University of Posts and Telecommunications
  • Chuan Shi Beijing University of Posts and Telecommunications
  • Bai Wang Beijing University of Posts and Telecommunications
  • Xiaoye Tan Alibaba Group
  • Yanghua Li Alibaba Group
  • Shaojian He Alibaba Group




Business/Marketing/Advertising/E-commerce, Social Networks, Web Search & Information Retrieval, Graph-based Machine Learning


The prosperous development of social e-commerce has spawned diverse recommendation demands, and accompanied a new recommendation paradigm, share recommendation. Significantly different from traditional binary recommendations (e.g., item recommendation and friend recommendation), share recommendation models ternary interactions among 〈 User, Item, Friend 〉 , which aims to recommend a most likely friend to a user who would like to share a specific item, progressively becoming an indispensable service in social e-commerce. Seamlessly integrating the social relations and purchase behaviours, share recommendation improves user stickiness and monetizes the user influence, meanwhile encountering three unique challenges: rich heterogeneous information, complex ternary interaction, and asymmetric share action. In this paper, we first study the share recommendation problem and propose a heterogeneous graph neural network based share recommendation model, called HGSRec. Specifically, HGSRec delicately designs a tripartite heterogeneous GNNs to describe the multifold characteristics of users and items, and then dynamically fuses them via capturing potential ternary dependency with a dual co-attention mechanism, followed by a transitive triplet representation to depict the asymmetry of share action and predict whether share action happens. Offline experiments demonstrate the superiority of the proposed HGSRec with significant improvements (11.7%-14.5%) over the state-of-the-arts, and online A/B testing on Taobao platform further demonstrates the high industrial practicability and stability of HGSRec.




How to Cite

Ji, H., Zhu, J., Wang, X., Shi, C., Wang, B., Tan, X., Li, Y., & He, S. (2021). Who You Would Like to Share With? A Study of Share Recommendation in Social E-commerce. Proceedings of the AAAI Conference on Artificial Intelligence, 35(1), 232-239. https://doi.org/10.1609/aaai.v35i1.16097



AAAI Technical Track on Application Domains