A-HSG: Neural Attentive Service Recommendation based on High-order Social Graph

Abstract

With the widespread application of Service-Oriented Architecture, the quantity of web services keeps increasing rapidly over the Internet. Providing personalized service recommendation to users remains to be an important research topic. Recent studies have proved social connections helpful for modeling users' potential preference thus improving the performance of service recommendation. To date, however, one special type of social relation, called high-order social relation, has not been thoroughly studied. In reality, a user's preference may not only be affected by the user's direct neighbors, but also indirect ones. Furthermore, such influences may not remain static in the context of various attentions. To tackle such issues, we have developed a novel neural Attentive network based on High-order Social Graph (A-HSG) toward offering social-aware service recommendation. First, a graph convolution-based, multi-hop propagation module is devised to extract the high-order similarity signals from users' local social networks, and inject them into the users' general representations. Second, a neighbor-level attention module is constructed to adaptively select informative neighbors to model the users' specific preference. Extensive experiments over a real-life service dataset show that A-HSG outperforms baseline methods in terms of prediction accuracy.