Relational metric learning with high-order neighborhood interactions for social recommendation

摘要

Social information has been widely incorporated into traditional recommendation systems to alleviate the data sparsity and cold-start issues. However, existing social recommend methods typically have two common limitations: (a) they learn a unified representation for each user involved in both item and social domains, which is insufficient for fine-grained user modeling. (b) They ignore the high-order neighborhood information encoded in both user–item interactions and social relations. To overcome these two limitations, this paper proposes a novel social recommend method, SoHRML, based on social relations under the metric learning framework. Specifically, user–item interactions and social relations are modeled as two types of relation vectors, with which each user could be translated to both multiple item-aware and social-aware representations. In addition, to capture the rich information encoded in local neighborhoods, we model the relation vectors by high-order neighborhood interactions (HNI). In each domain, we design a dual layer-wise neighborhood aggregation (LNA) structure that contains dual graph attention networks (GATs) to aggregate the neighborhoods of users or items. Both high-order information encoded in user–item interactions and social relations can be captured by stacking the layer-wise structure. Extensive experimental results on three practical datasets demonstrate the superiority of the proposed model, especially under the cold-start scenarios. The performance gains over the best baseline are 0.51% to 3.31% on two ranking-based metrics.