Title: Tag recommendation based on topic hierarchy of folksonomy

Authors: Han Xue; Bing Qin; Ting Liu; Shen Liu

Addresses: Harbin Engineering University Library, Harbin Engineering University, Harbin, 150001, China ' School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China ' School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China ' School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China

Abstract: As a recommendation problem, tag recommendation has been receiving increasing attention from both the business and academic communities. Traditional recommendation methods are inappropriate for folksonomy because the basis of such mechanism remains un-updated in time owing to the bottleneck of knowledge acquisition. Therefore, we propose a novel method of tag recommendation based on the topic hierarchy of folksonomy. The method applies the topic tag hierarchy constructed automatically from folksonomy to tag recommendation using the proposed strategy. The method can improve the quality of folksonomy and can evaluate the topic tag hierarchy through tag recommendation. The precision of tag recommendation reaches 0.892. The experimental results show that the proposed method significantly outperforms state-of-the-art methods (t-test, p-value < 0.0001) and demonstrates effectiveness with respect to data sources on tag recommendation.

Keywords: tag recommendation; topic hierarchy; folksonomy.

DOI: 10.1504/IJCSE.2019.103249

International Journal of Computational Science and Engineering, 2019 Vol.20 No.1, pp.49 - 58

Received: 10 Jun 2016
Accepted: 23 Apr 2017
Published online: 23 Oct 2019 *

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