DUSKG: A fine-grained knowledge graph for effective personalized service recommendation

Abstract

Abstract The last decade has witnessed the rapid development of service-oriented computing, resulting in a tremendous growth of services which further calls for novel approaches for efficient and effective service recommendations. Various types of data can be used for service recommendations, such as user feedback on services, service profile, user profile, etc. In traditional recommendation algorithms, such data is usually handled separately and in the form of matrix. Since there are underlying logical relations between the data, we conjecture that deep convergence of all the data and full considerations of such logical relations would help boost accuracy of service recommendations. By a comprehensive survey on various existing data convergence methods, we found that knowledge graph had been a typical approach to describe the different relations between data. In this paper, we propose an elegant, natural and compact data representation model incorporating such kinds of logical relations, namely D omain-oriented U ser and S ervice Interaction K nowledge G raph (DUSKG). Three types of entities ( U s e r , S e r v i c e and S e r v i c e V a l u e F e a t u r e (VF)), five types of fine-grained relations (FOCUSON, BELONGTO, USIMILAR, SSIMILAR and FSIMILAR), and weight vectors that quantitatively delineate these relations are elaborately defined. An approach for constructing DUSKG from service data is presented. Extracted from DUSKG, each user’s value preference is then represented in terms of VFs. Five personalized service recommendation methods are presented in terms of such value preferences and various relations in DUSKG. To verify the proposed methods, a set of experiments are conducted on Yelp dataset. Experimental results show that our approaches achieve better recommendation performance compared to the state-of-the-art methods, and among the five ones, HR-DUSKG (Hybrid Recommendation Depending on DUSKG) exhibits the best performance with quite good achievements but least calculation time.