Personalised push method for sports goods purchase information in the context of marketing

Ziya Wang and Fei Gao

International Journal of Product Development, 2025, vol. 29, issue 1, 1-16

Abstract: To solve the issues of low satisfaction and low unique visitor (UV) click rate in the existing personalised push methods of commodity purchase information, this paper proposes a personalised push method for sports goods purchase information in the context of marketing. By utilising crawler technology, an online shopping platform users' behaviour data set for purchasing sporting goods is established. A generalised hierarchical tree of user portrait attribute tags is constructed, and the browsing time of sporting goods containing a certain tag is calculated to allocate the membership degree of sporting goods and mine user interest preferences. The Pearson similarity algorithm is applied to construct the similarity matrix for personalised push, enabling personalised push of sporting goods purchase information. Experiments demonstrate that the user satisfaction rate using this method consistently remains above 88%. Furthermore, the highest UV click rate achieved is 7.56%, indicating a successful push effect.

Keywords: sports goods; user portrait; generalised hierarchical tree; membership degree; Pearson similarity. (search for similar items in EconPapers)
Date: 2025
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