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

Authors

  • 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

Keywords:

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

Abstract

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.

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Published

2021-05-18

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. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16097

Issue

Section

AAAI Technical Track on Application Domains