Context-aware QoS prediction for web service recommendation and selection |
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| Affiliation: | 1. College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang 310027, China;2. School of Computer Science, Colorado Technical University, Colorado Springs, CO 80907, USA;3. School of Software, Xidian University, Xi’an, Shaanxi 710071, China;1. School of Computer and Communication Engineering, University of Science and Technology Beijing (USTB), Beijing 100083, China;2. Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing 100083, China;1. School of Information Science and Engineering, Qufu Normal University, China;2. Institute of Natural and Mathematical Sciences, Massey University, New Zealand;3. Key Laboratory of Hunan Province for Mobile Business Intelligence, Hunan University of Commerce, China;4. Computer Science and Creative Technologies Department, University of the West of England, UK;5. Faculty of Information and Communication Technologies, Swinburne University of Technology, Australia;6. School of Computer and Software, Nanjing University of Information Science and Technology, China;1. School of Information Science and Engineering, Qufu Normal University, China;2. School of Information and Safety Engineering, Zhongnan University of Economics and Law, China;3. Department of Computing, Macquarie University, Australia;4. Computer Science and Creative Technologies Department, University of the West of England, UK;5. State Key Laboratory for Novel Software Technology, Department of Computer Science and Technology, Nanjing University, China;6. School of Computer Science and Engineering, Hunan University of Science and Technology, China;7. College of Information and Electrical Engineering, China Agricultural University, China |
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| Abstract: | QoS prediction is one of the key problems in Web service recommendation and selection. The context information is a dominant factor affecting QoS, but is ignored by most of existing works. In this paper, we employ the context information, from both the user side and service side, to achieve superior QoS prediction accuracy. We propose two novel prediction models, which are capable of using the context information of users and services respectively. In the user side, we use the geographical information as the user context, and identify similar neighbors for each user based on the similarity of their context. We study the mapping relationship between the similarity value and the geographical distance. In the service side, we use the affiliation information as the service context, including the company affiliation and country affiliation. In the two models, the prediction value is learned by the QoS records of a user (or a service) and the neighbors. Also, we propose an ensemble model to combine the results of the two models. We conduct comprehensive experiments in two real-world datasets, and the experimental results demonstrate the effectiveness of our models. |
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