Time-aware Service Recommendation with Social-powered Graph Hierarchical Attention Network

Abstract

Driven by Service-Oriented Computing techniques, time-aware service recommendation aims to support personalized mashup development, adapting to the rapid shifts of users' dynamic preferences. Recent studies have revealed that users' social connections may help better model their dynamic preferences. However, two phenomena exist to influence users' dynamic preferences of service selection. First, users and their friends may only share preferences in certain services, which means not every service in the friends' consumed mashups has the same impact on a target user's dynamic preference. Second, for a target user, friends in his social network with similar interests and behaviors may contribute more influence intensities. To cover the above phenomena synergistically, this paper proposes a Social-powered Graph Hierarchical Attention Network (SGHAN), as a deep learning model capable of learning similar behaviors from proper friends during mashup development. SGHAN is powered by the reciprocity between its two core components: a service-level attentional encoder captures users' interested services in friends' mashups, while a friend-level graph attention network selects informative friends and propagates the friends' social influences. Extensive experiments show that the SGHAN model consistently outperforms the state-of-the-art methods in terms of prediction accuracy for mashup creation.