Hybrid collaborative filtering model for consumer dynamic service recommendation based on mobile cloud information system |
| |
| Institution: | 1. School of Economics and Management, Changzhou Vocational Institute of Mechatronic Technology, China;2. The Institute for Industrial Economy of Intelligent Manufacturing, China;3. Changzhou Electronic Commerce Research Institute, China;4. Changzhou Key Laboratory of Industrial Internet and Data Intelligence, China;5. The Centre for Chinese Studies, Sichuan University, China;1. School of Information and Communication Engineering, Hunan Institute of Science and Technology, Hunan, China;2. Machine Vision & Artificial Intelligence Research Center, Hunan Institute of Science and Technology, Hunan, China;1. Ryerson University;2. Arizona State University;3. Illinois Institute of Technology;4. University of Guelph;1. AGH University of Science and Technology, 30 Mickiewicza Ave, Kraków 30-059, Poland;2. VSB Technical University of Ostrava, 17. listopadu 2172/15, Ostrava-Poruba 708 00, Czech Republic |
| |
| Abstract: | The rapid development of the web has led to a considerable increase in information dissemination. Recently, personalized web service recommendation has become a popular research area in service computing. Research on web service recommendation systems mainly addresses two problems: prediction and completion of sparse QoS data, and the user's personalized recommendation. To address the issue of high data sparsity and low recommendation accuracy in the traditional service recommendation models under mobile cloud, this study presents a hybrid collaborative filtering model for consumer service recommendation based on mobile cloud by introducing user preferences. The example verified that the service recommendation based on the model can effectively reduce the data sparsity and increase the accuracy of the prediction. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
