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Wednesday, 7 February 2018

Location-Aware and Personalized Collaborative Filtering

Location-Aware and Personalized Collaborative Filtering

Abstract— Collaborative Filtering (CF) is widely employed for making Web service recommendation. CF-based Web service recommendation aims 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. Firstly, existing QoS prediction methods seldom consider personalized influence of users and services when measuring the similarity between users and between services. Secondly, Web service QoS factors, such as response time and throughput, usually depends on the locations of Web services and users. However, existing Web service QoS prediction methods seldom took this observation into consideration. Location-Aware and Personalized Collaborative Filtering project, we propose a location-aware personalized CF method for Web service recommendation. The proposed method leverages both locations of users and Web services when selecting similar neighbors for the target user or service. The method also includes an enhanced 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 conduct a set of comprehensive experiments using a real-world Web service dataset. The experimental results indicate that our approach improves the QoS prediction accuracy and computational efficiency significantly, compared to previous CF-based methods. Location-Aware and Personalized Collaborative Filtering
Location-Aware and Personalized Collaborative Filtering project presents a personalized location-aware collaborative filtering method for QoS-based Web service recommendation. Aiming at improving the QoS prediction performance, we take into account the personal QoS characteristics of both Web services and users to compute similarity between them. 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. In the future, we will take more detailed location information into consideration for QoS prediction, such as the Internet’s AS topology. We will also consider incorporating the time factor into QoS prediction, and plan to obtain bigger datasets for evaluating our methods.

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