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.