High-Order Social Graph Neural Network for Service Recommendation

Driven by proliferation of the Service-Oriented Architecture (SOA), the quantity of published software services and their users keeps increasing rapidly in the service ecosystem; thus, personalized service selection and recommendation has remained a hot topic. Recent studies have revealed that users' social connections may help better model their potential behaviors. Therefore, in this paper, we study how users' high-order social networks may help improve service recommendation as well as its explainability. Two observations are set forth. First, a user's service preference may be influenced by his trusted users, whom in turn influenced by their social connections. Second, such chained influences will not remain static and equally-weighted, as a user's confidence over his social relations may vary confronted with different targeted services. We thus introduce a novel High-order Social Graph Neural Network (HSGNN) to support social-aware service recommendation. The key idea of the model is a graph convolution-based, multi-hop propagation module devised to extract the high-order social similarity signals from users' local social networks, and encode them into the users' general representations. Afterwards, a neighbor-level attention module is constructed to adaptively select informative neighbors to model the users' specific preference. Extensive experiments in a real-world service dataset show that our HSGNN makes service recommendation more accurately, i.e., by 4.71% in terms of normalized discounted cumulative gain (NDCG), than state-of-the-art baseline methods.