A General Recommendation Model for Heterogeneous Networks

Heterogeneous networks refer to the networks comprising multiple types of entities as well as their interaction relationships. They arise in a great variety of domains, for example, event-based social networks Meetup and Plancast, and DBLP. Recommendation is a useful task in these heterogeneous network systems. Although many recommendation algorithms are proposed for heterogeneous data, none of them is able to explicitly model the influence strength between different types of entities, which is useful not only for achieving higher recommendation accuracy but also better understanding the role of each entity type in recommendation problems. Moreover, many of those algorithms are designed for a particular task, and hence it is challenging to apply them in other problems. In this paper, we propose a graph-based model, called HeteRS, which can solve general recommendation problems on heterogeneous networks. Our method models the rich information with a heterogeneous graph and considers the recommendation problem as a query-dependent node proximity problem. To address the challenging issue of weighting the influences between different types of entities, we propose a learning scheme to set the influence weights between different types of entities in recommendation. Experimental results on real-world datasets demonstrate that our proposed method significantly outperforms the baseline methods in our experiments for all the recommendation tasks, and the learned influence weights help understanding user behaviors.