Attention-based explainable friend link prediction with heterogeneous context information

Abstract

Friend link prediction is an important research problem in recommender systems. Existing network embedding and knowledge embedding methods mainly consider the structural relationships of entities, ignoring the explanatory role of text contents. In this paper, we present an explainable friend link recommendation method that leverages direct and indirect similarity of user pairs via fusion embedding of heterogeneous context information. In social networks, while considering user content interests, first, a fusion user embedding method was developed by incorporating external knowledge semantics. Second, for a user pair, we calculate their direct similar relationship using fusion user embeddings. Additionally, based on intermediate neighbors, we compute their indirect similar relationship by using an attention mechanism, which explains neighbors’ bootable and transitive influences for learning the social relationship of user pairs. Then, a hybrid personalized and neighbor attention model for friend link prediction was proposed by considering direct and indirect factors. Finally, experiments were conducted on the Sina Weibo datasets, which indicates that the proposed method effectively predicts the friends of users and provides a good interpretation for link prediction recommendation results.