发表状态 | 已发表Published |
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
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ISSN/eISSN | 1550-1329 |
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 |
DOI | 10.1177/1550147717749745 |
URL | 查看来源 |
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 | |
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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