Implicit relation-aware social recommendation with variational auto-encoder

Integrating social networks as auxiliary information shows effectiveness in improving the performance for a recommendation task. Typical models usually characterize the user trust relationship as a binary adjacent matrix derived from a social graph, which basically only incorporates neighborhood interactions, and then encodes the trust values of different individuals with the same value. Such methods fail to capture the implicit high-order relations hidden under a graph structure, and thereby ignore the impact of indirect influencers. To address the aforementioned problems, we present an I mplicit T rust R elation-A ware model (ITRA) based on Variational Auto-Encoder (VAE). ITRA adopts an attention module to feed the weighted trust embedding information into an inherited non-linear VAE structure. In this sense, ITRA could provide recommendations by reconstructing a non-binary adjacency social matrix with implicit high-order interactions from both indirect key opinion leaders and explicit connections from neighbors. The extensive experiments conducted on three datasets illustrate that ITRA could achieve a better performance compared to the state-of-the-art methods.