Meta-path based service recommendation in heterogeneous information networks

In the scenario of service recommendation, there are multiple object types (e.g. services, mashups, categories, contents and providers) and rich relationships among these objects, which naturally constitute a heterogeneous information network (HIN). In this paper, we propose to recommend services for mashup creation by exploiting different types of relationships in service related HIN. Specifically, we first introduce meta-path based measure for similarity estimation between mashups along different types of paths in HIN. We then design a recommendation model based on collaborative filtering and meta-path based similarities, and employ Bayesian ranking based optimization algorithm for model learning. Comprehensive experiments based on real data demonstrate the effectiveness of the HIN based service recommendation approach.