Joint Modeling Users, Services, Mashups, and Topics for Service Recommendation

With an increasing number of Web services on-line, service recommendation becomes an important approach to help user discover suitable services. However, current service recommendation approaches only consider user's historical preferences and functional or non-functional properties of services. The compositional side of services to form mashups is overlooked. However, the service composition information is a valuable information for recommendation since it gives an implicit measure of relatedness of services. In this paper, we recognize the importance of mashups on service recommendation and propose to jointly model historical preference of users on mashups and services, compositional information of services as mashups, and functional information of services and mashups in a single framework. Specifically, we extract topics from functional descriptions of services and model the relations between user, mashup and service and topics as a quadripartite graph. We use a personalized ranking on graph algorithm to learn the proposed model and simultaneously recommend services and mashups for user. We conduct a comprehensive experimental study using real world data from ProgrammbleWeb and results show that our recommendation method outperforms other representative recommendation approaches.