Title: A data mining method based on label mapping for long-term and short-term browsing behaviour of network users

Authors: Xiangyuan Liu

Addresses: College of Culture Communication and Art Design, Hunan College of Information, Changsha, 410000, China

Abstract: In order to improve the speedup and recognition accuracy of the recognition process, this paper designs a data mining method based on label mapping for long-term and short-term browsing behaviour of network users. First, after removing the noise information in the behaviour sequence, calculate the similarity of behaviour characteristics. Then, multi-source behaviour data is mapped to the same dimension, and a behaviour label mapping layer and a behaviour data mining layer are established. Finally, the similarity of the tag matrix is calculated based on the similarity calculation results, and the mining results are output using SVM binary classification process. Experimental results show that the acceleration ratio of this method exceeds 0.9; area under curve receiver operating characteristic curve (AUC-ROC) value increases rapidly in a short time, and the maximum value can reach 0.95, indicating that the mining precision of this method is high.

Keywords: long-term and short-term browsing behaviour; data mining; lifting small transformation method; data mapping; label mapping; SVM secondary classification.

DOI: 10.1504/IJITM.2024.139568

International Journal of Information Technology and Management, 2024 Vol.23 No.3/4, pp.219 - 231

Received: 17 Nov 2022
Accepted: 27 Feb 2023
Published online: 04 Jul 2024 *

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