4.7 Article

Location-Aware and Personalized Collaborative Filtering for Web Service Recommendation

Journal

IEEE TRANSACTIONS ON SERVICES COMPUTING
Volume 9, Issue 5, Pages 686-699

Publisher

IEEE COMPUTER SOC
DOI: 10.1109/TSC.2015.2433251

Keywords

Web services; service recommendation; QoS prediction; collaborative filtering; location-aware

Funding

  1. National Natural Science Foundation of China [61100054, 61272063]
  2. National Science AMP
  3. Technology Pillar Program of China [2015BAF32B01]
  4. Guangdong Natural Science Foundation [2014A030313151]
  5. Open Foundation of State key Laboratory of Networking and Switching Technology (Beijing University of Posts and Telecommunications) [SKLNST-2013-1-21]

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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. First, existing QoS prediction methods seldom consider personalized influence of users and services when measuring the similarity between users and between services. Second, 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. In this paper, 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.

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