Location-Aware and Personalized Collaborative Filtering For Web Service Recommendation

Collaborative filtering explores techniques for matching people with similar interests and making personalized recommendations on the web. The Collaborative Filtering (CF) is widely employed for making Web service recommendation. The main aim is to predict missing QoS (Quality-of-Service) values of Web services. Although several CF-based Web service QoS prediction methods have been proposed in recent years, the performance still needs significant improvement. In this the Quality of Service (QoS) prediction methods rarely consider personalized influence of users and services and it consider Web service QoS factors, such as response time and throughput, usually depends on the locations of Web services and users. In this paper, we propose a location-aware personalized CF method for Web service recommendation. The proposed method sways both locations of users and Web services when selecting similar neighbors for the target user or service it also includes an intensify similarity measurement for users and Web services, by taking into account the personalized influence of them. To evaluate the performance of our proposed method, We conducted a set of comprehensive experiments employing a real-world Web service dataset, which demonstrated that the proposed Web service QoS prediction method significantly outperforms previous well-known methods. We also incorporate the locations of both Web services and users into similar neighbor selection, for both Web services and users. Comprehensive experiments conducted on a real Web service dataset indicate that our method significantly outperforms previous CF-based Web service recommendation methods and it improves the QOS prediction performance, we take into account the personal QoS characteristics of the both web service and user to compute similarity between them.