Meta-Path Based Service Recommendation In Heterogeneous Information Networks
Service-Oriented Computing: 14th International Conference, ICSOC 2016, Banff, AB, Canada, October 10-13, 2016, Proceedings(2016)
摘要
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.
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