Who You Would Like to Share With? A Study of Share Recommendation in Social E-commerce
Keywords:Business/Marketing/Advertising/E-commerce, Social Networks, Web Search & Information Retrieval, Graph-based Machine Learning
AbstractThe 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