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A folksonomy-based collaborative filtering method for crowdsourcing knowledge-sharing communities

Kangqu Zhou (College of Mechanical Engineering, Chongqing University of Technology, Chongqing, China)
Chen Yang (College of Mechanical Engineering, Chongqing University of Technology, Chongqing, China)
Lvcheng Li (School of Entrepreneurship, Wuhan University of Technology, Wuhan, China)
Cong Miao (National Institute of Development Administration, Bangkok, Thailand)
Lijun Song (College of Mechanical Engineering, Chongqing University of Technology, Chongqing, China)
Peng Jiang (College of Mechanical Engineering, Chongqing University of Technology, Chongqing, China)
Jiafu Su (National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Chongqing, China) (International College, Krirk University, Bangkok, Thailand)

Kybernetes

ISSN: 0368-492X

Article publication date: 15 October 2021

Issue publication date: 17 January 2023

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Abstract

Purpose

This paper proposes a recommendation method that mines the semantic relationship between resources and combine it with collaborative filtering (CF) algorithm for crowdsourcing knowledge-sharing communities.

Design/methodology/approach

First, structured tag trees are constructed based on tag co-occurrence to overcome the tags' lack of semantic structure. Then, the semantic similarity between tags is determined based on tag co-occurrence and the tag-tree structure, and the semantic similarity between resources is calculated based on the semantic similarity of the tags. Finally, the user-resource evaluation matrix is filled based on the resource semantic similarity, and the user-based CF is used to predict the user's evaluation of the resources.

Findings

Folksonomy is a knowledge classification method that is suitable for crowdsourcing knowledge-sharing communities. The semantic similarity between resources can be obtained according to the tags in the folksonomy system, which can be used to alleviate the data sparsity and cold-start problems of CF. Experimental results show that compared with other algorithms, the algorithm in this paper performs better in mean absolute error (MAE) and F1, which indicates that the proposed algorithm yields better performance.

Originality/value

The proposed folksonomy-based CF method can help users in crowdsourcing knowledge-sharing communities to better find the resources they need.

Keywords

Acknowledgements

This work is supported by the National Key Research and Development Program of China (Grant No. 2018YFB1700800) and the Youth Foundation of Ministry of Education of China (19YJC630141). Their support is greatly appreciated.

Conflicts of interest: The authors declare no conflicts of interest.

Citation

Zhou, K., Yang, C., Li, L., Miao, C., Song, L., Jiang, P. and Su, J. (2023), "A folksonomy-based collaborative filtering method for crowdsourcing knowledge-sharing communities", Kybernetes, Vol. 52 No. 1, pp. 328-343. https://doi.org/10.1108/K-04-2021-0263

Publisher

:

Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited

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