A Feedback-corrected Collaborative Filtering for Personalized Real-world Service Recommendation

  • Shuai Zhao State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications
  • Yang Zhang State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications
  • Bo Cheng State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications
  • Jun-liang Chen State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications

Abstract

The emergence of Internet of Things (IoT) integrates the cyberspacewith the physical space. With the rapid development of IoT, large amounts of IoTservices are provided by various IoT middleware solutions. So, discovery and selectingthe adequate services becomes a time-consuming and challenging task. This paperproposes a novel similarity-measurement for computing the similarity between servicesand introduces a new personalized recommendation approach for real-world servicebased on collaborative filtering. In order to evaluate the performance of proposedrecommendation approach, large-scale of experiments are conducted, which involvesthe QoS-records of 339 users and 5825 real web-services. The experiments resultsindicate that the proposed approach outperforms other compared approaches in termsof accuracy and stability.

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Published
2014-04-04
How to Cite
ZHAO, Shuai et al. A Feedback-corrected Collaborative Filtering for Personalized Real-world Service Recommendation. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 9, n. 3, p. 356-369, apr. 2014. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/1085>. Date accessed: 13 july 2020. doi: https://doi.org/10.15837/ijccc.2014.3.1085.

Keywords

Internet of Things, service recommendation, similarity measurement, collaborative filtering