近日，实验室两篇研究论文 “Location-aware and personalized collaborative filtering for Web service recommendation”和“Diversifying Web Service Recommendation Results via Exploring Service Usage History” 相继被IEEE Transactions on Services Computing正式录用。这是实验室首次在IEEE Transactions系列期刊上发表有关服务计算的学术论文，是实验室的一个新突破，将为实验室在服务计算方向的研究奠定重要基础和掀开新的篇章。所发表论文简要信息如下：
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标题: Location-aware and personalized collaborative filtering for Web service recommendation
作者: Jianxun Liu, Mingdong Tang, Zibin Zheng, Xiaoqing (Frank) Liu, and Saixia Lyu
来源出版物: IEEE Transactions on Services Computing
DOI: 10.1109/TSC.2015.2433251 ISSN：1939-1374
摘要：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. 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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标题: Diversifying Web Service Recommendation Results via Exploring Service Usage History
作者: Guosheng Kang, Mingdong Tang, Jianxun Liu, Xiaoqing (Frank) Liu, and Buqing Cao
来源出版物: IEEE Transactions on Services Computing
DOI: 10.1109/TSC.2015.2415807 ISSN：1939-1374
摘要：The last decade has witnessed a tremendous growth of Web services as a major technology for sharing data, computing resources, and programs on the Web. With the increasing adoption and presence of Web services, design of novel approaches for effective Web service recommendation to satisfy users’ potential requirements has become of paramount importance. Existing Web service recommendation approaches mainly focus on predicting missing QoS values of Web service candidates which are interesting to a user using collaborative filtering approach, content-based approach, or their hybrid. These recommendation approaches assume that recommended Web services are independent to each other, which sometimes may not be true. As a result, many similar or redundant Web services may exist in a recommendation list. In this paper, we propose a novel Web service recommendation approach incorporating a user’s potential QoS preferences and diversity feature of user interests on Web services. User’s interests and QoS preferences on Web services are first mined by exploring the Web service usage history. Then we compute scores of Web service candidates by measuring their relevance with historical and potential user interests, and their QoS utility. We also construct a Web service graph based on the functional similarity between Web services. Finally, we present an innovative diversity-aware Web service ranking algorithm to rank the Web service candidates based on their scores, and diversity degrees derived from the Web service graph. Extensive experiments are conducted based on a real world Web service dataset, indicating that our proposed Web service recommendation approach significantly improves the quality of the recommendation results compared with existing methods.
注：IEEE Transactions on Service Computing系服务计算领域顶级期刊，CCF B类期刊，2013-2014最新影响因子1.985，涉及的主要研究方向为计算机软件、服务计算和云计算等。
（编辑 张婷婷）
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