An Effective Web Service Recommendation Method Based on Personalized Collaborative Filtering

Abstract

Collaborative filtering is one of widely used Web service recommendation techniques. There have been several methods of Web service selection and recommendation based on collaborative filtering, but seldom have they considered personalized influence of users and services. In this paper, we present an effective personalized collaborative filtering method for Web service recommendation. A key component of Web service recommendation techniques is computation of similarity measurement of Web services. Different from the Pearson Correlation Coefficient (PCC) similarity measurement, we take into account the personalized influence of services when computing similarity measurement between users and personalized influence of services. Based on the similarity measurement model of Web services, we develop an effective Personalized Hybrid Collaborative Filtering (PHCF) technique by integrating personalized user-based algorithm and personalized item-based algorithm. We conduct series of experiments based on real Web service QoS dataset WSRec [11] which contains more than 1.5 millions test results of 150 service users in different countries on 100 publicly available Web services located all over the world. Experimental results show that the method improves accuracy of recommendation of Web services significantly.