CASR-TSE: Context-Aware Web Services Recommendation for Modeling Weighted Temporal-Spatial Effectiveness.

  • SCI-E
  • EI
作者: Fan, Xiaoliang;Hu, Yakun;Zheng, Zibin;Wang, Yujie;Brezillon, Patrick;Chen, Wenbo
作者机构: School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu China (e-mail: huyk14@lzu.edu.cn)
School of Data and Computer Science, Sun Yat-Sen University, 26469 Guangzhou, Guangdong China (e-mail: zibin.gil@gmail.com)
School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu China (e-mail: wangyujie15@lzu.edu.cn)
Fujian Key Laboratory of Sensing and Computing for Smart City, Xiamen University, Xiamen University, 12466 Xiamen, Fujian China (e-mail: fanxiaoliang@lzu.edu.cn)
School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu China (e-mail: chenwb@lzu.edu.cn)
LIP6, University Pierre and Marie Curie, Paris, Paris France 75005 (e-mail: Patrick.Brezillon@lip6.fr)
语种: 英文
关键词: Computational model - Context- awareness - Context-aware recommender systems - Personalized recommendation - Temporal and spatial - Temporal-spatial correlations - Time factors - Web service recommendations
期刊: IEEE Transactions on Services Computing
ISSN: 1939-1374
年: 2021
摘要: Recent years have witnessed the growing research interest in the Context-Aware Recommender System (CARS). Specifically, CARS for Web service provides opportunities for exploring the important role of temporal and spatial contexts, separately. Although many CARS approaches have been investigated in recent years, they do not fully address the potential of temporal-spatial correlations in order to make personalized recommendation. In this paper, the Context-Aware Services Recommendation based on Temporal-Spatial Effectiveness (named CASR-TSE) method is proposed. We first model the effectiveness of spatial correlations between the user&amp;#x0027;s location and the service&amp;#x0027;s location on user preference expansion before the similarity computation. Second, we present an enhanced temporal decay model incorporating the weighted rating effect to improve the prediction accuracy in similarity computation. Finally, we evaluate the CASR-TSE method on a real-world Web services dataset. Experimental results show that the proposed method significantly outperforms existing approaches, and thus it is much more effective than traditional recommendation techniques for personalized Web service recommendation.<br/> IEEE

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CASR-TSE: Context-Aware Web Services Recommendation for Modeling Weighted Temporal-Spatial Effectiveness.
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