Title: A social network user behaviour data recommendation system based on fuzzy partition clustering

Authors: Han Ge; Shumin Ren; Hongliang Zhang

Addresses: Network and New Media, Beihua University, Jilin, Jilin City, China ' Chinese Language & Literature, Beihua University, Jilin, Jilin City, China ' Network and New Media, Beihua University, Jilin, Jilin City, China

Abstract: To address the problems of low recommendation accuracy and recall in existing recommendation methods, this paper proposes a social network user behaviour data recommendation system based on fuzzy partition clustering. Firstly, design the hardware of a social network user behaviour data recommendation system. Secondly, collect topology data of social network user behaviour and extract preference features of social network users browsing certain category label content. Once again, construct a fuzzy partition clustering sample grid to cluster social network user preference features. Finally, based on the Pearson similarity algorithm, social network user behaviour data recommendation is implemented. The experimental results show that the proposed recommendation system has an average accuracy of 90% and an average recall rate of 90.83%, indicating good application performance.

Keywords: fuzzy partition clustering; social network users; user behaviour; recommendation system; Pearson similarity.

DOI: 10.1504/IJCAT.2024.141355

International Journal of Computer Applications in Technology, 2024 Vol.74 No.1/2, pp.10 - 18

Received: 01 Nov 2023
Accepted: 13 Feb 2024
Published online: 09 Sep 2024 *

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