Title: A collaborative filtering recommendation method based on TagIEA expert degree model

Authors: Weimin Li; Bin Wang; Jianbo Zou; Jinfang Sheng

Addresses: School of Information Science and Engineering, Central South University, Changsha, Hunan, China; Department of Information Science and Engineering, Hunan University of Humanities, Science and Technology, Loudi, Hunan, China ' School of Information Science and Engineering, Central South University, 932 Lushan South Road, Changsha 410083, Hunan, China ' School of Information Science and Engineering, Central South University, 932 Lushan South Road, Changsha 410083, Hunan, China ' School of Information Science and Engineering, Central South University, 932 Lushan South Road, Changsha 410083, Hunan, China

Abstract: In recent years, social networking services and e-commerce have been developing rapidly. The research of recommending in e-commerce service mainly focused on using the collaborative filtering algorithm. But the algorithm had the limitations of data sparsity and cold start. This paper presents a model using TagIEA expert degree metrics in the context of social e-commerce services, where tag and expert degree information are integrated into the collaborative filtering algorithm. The comprehensive recommendation based on the TagIEA expert degree can effectively mitigate the problems of cold start and data sparsity. Finally, this paper verifies the effectiveness of the improved collaborative filtering algorithm by experiments.

Keywords: collaborative filtering; tag; expert degree; social networking services; SNSs; recommendation algorithm.

DOI: 10.1504/IJCSE.2017.084684

International Journal of Computational Science and Engineering, 2017 Vol.14 No.4, pp.321 - 329

Received: 12 Oct 2015
Accepted: 15 Dec 2015
Published online: 21 Jun 2017 *

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