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题名Integrated collaborative filtering recommendation in social cyber-physical systems
作者
发表日期2017-12-01
发表期刊International Journal of Distributed Sensor Networks
ISSN/eISSN1550-1329
卷号13期号:12
摘要

Cyber-physical systems are becoming part of our daily life, and a large number of data are generated at such an unprecedented rate that it becomes larger than ever before in social cyber-physical systems. As a consequence, it is highly desired to process these big data so that meaningful knowledge can be extracted from those vast and diverse data. Based on those large-scale data, using collaborative filtering recommendation methods to recommend some valuable clients or products for those e-commerce websites or users is considered as an effective way. In this work, we present an integrated collaborative filtering recommendation approach that combines item ratings, user ratings, and social trust for making better recommendations. In contrast to previous collaborative filtering recommendation works, integrated collaborative filtering recommendation approach takes full advantage of the correlation between data and takes into consideration the similarity between items, the similarity between users and two kinds of trust among users to select nearest neighbors of both users and items providing the most valuable information for recommendation. On the basis of neighbors selected, integrated collaborative filtering recommendation provides an approach combining two aspects to recommend valuable and suitable items for users. And the concrete process is illustrated as following: (1) the potentially interesting items are obtained by the shopping records of neighbors of a certain user, (2) the potentially interesting items are figured out according to the item neighbors of those items of the user, and (3) determine a few most interesting items combining the two sets of potential items obtained from previous process. A large number of experimental results show that the proposed integrated collaborative filtering recommendation approach can effectively enhance the recommendation performance in terms of mean absolute error and root mean square error. Integrated collaborative filtering recommendation approach could reduce mean absolute error and root mean square error by up to 27.5% and 15.7%, respectively.

关键词integrated collaborative filtering recommendation integrated collaborative filtering recommendation approach recommendation performance Social cyber-physical systems trust
DOI10.1177/1550147717749745
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收录类别SCIE
语种英语English
WOS研究方向Computer Science ; Telecommunications
WOS类目Computer Science, Information Systems ; Telecommunications
WOS记录号WOS:000418649400001
Scopus入藏号2-s2.0-85039919350
引用统计
被引频次:32[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符https://repository.uic.edu.cn/handle/39GCC9TT/7213
专题个人在本单位外知识产出
通讯作者Liu, Anfeng
作者单位
1.School of Information Science and Engineering, Central South University, Changsha, China
2.School of Computer Science, Colorado Technical University, Colorado Springs, United States
3.Department of Computer Science and Technology, Huaqiao University, Xiamen, China
4.Hunan Normal University, Changsha, China
推荐引用方式
GB/T 7714
Xu, Jiachen,Liu, Anfeng,Xiong, Naixueet al. Integrated collaborative filtering recommendation in social cyber-physical systems[J]. International Journal of Distributed Sensor Networks, 2017, 13(12).
APA Xu, Jiachen, Liu, Anfeng, Xiong, Naixue, Wang, Tian, & Zuo, Zhengbang. (2017). Integrated collaborative filtering recommendation in social cyber-physical systems. International Journal of Distributed Sensor Networks, 13(12).
MLA Xu, Jiachen,et al."Integrated collaborative filtering recommendation in social cyber-physical systems". International Journal of Distributed Sensor Networks 13.12(2017).
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