A Novel Recommendation Service Method Based on Cloud Model and User Personality

The number of Internet Web services has become increasingly large recently. Cloud services consumers face a critical challenge in selecting services from abundant candidates. Due to the uncertainty of Web service QoS and the diversity of user characteristics, this paper proposes a Web service recommendation method based on cloud model and user personality (WSRCP), which employs cloud model similarity method to analyze the similarity of QoS feedback data among different users, to identify the user with high similarity to the potential user. Based on the QoS data of the users’ feedback, Finally, user characteristic attribute Web service recommendation is implemented by personalized collaborative filtering algorithm. The experimental results on the WS-Dream dataset show that our approach not only solves the drawbacks of the sparse user service, but also improves the recommend accuracy.