Author(s)

Baicheng Jiang

Corresponding Author

Baicheng Jiang

Abstract

This paper explores the optimization of product recommendation strategies on e-commerce platforms based on association rule mining. It first outlines the core concepts of association rule mining, the basic logic of e-commerce recommendations, and the value of their integration. It then analyzes existing issues in e-commerce recommendations, such as insufficient accuracy and serious homogenization. Subsequently, it proposes optimization directions from four aspects: improving the quality of data foundations, enhancing rule effectiveness, strengthening the matching degree between rules and user demands, and establishing a real-time iteration mechanism. Implementation paths are provided in terms of data, algorithms, recommendation logic, and dynamic updates. This strategy, by leveraging data-driven mining of implicit product associations, effectively addresses the shortcomings of traditional recommendations and achieves a two-way optimization of user needs and platform growth.

Keywords

Association Rule Mining; E-commerce Product Recommendation; Strategy Optimization; Data-Driven; Precise Matching