Application of an improved Apriori algorithm in a mobile e-commerce recommendation system

TLDR

The study aims to enhance mobile e‑commerce shopping convenience and reduce information overload by applying an improved Apriori algorithm to a recommendation system that improves data‑mining efficiency and addresses mobile interface limitations. The authors propose a mobile‑specific improved Apriori algorithm and implement it in a recommendation system, validating its effectiveness on a Taobao online dress shop. Experimental results show that the improved Apriori‑based system boosts data‑mining efficiency, achieves real‑time recommendation accuracy, and outperforms conventional methods in prediction accuracy.

Abstract

Purpose The purpose of this paper is to make the mobile e-commerce shopping more convenient and avoid information overload by a mobile e-commerce recommendation system using an improved Apriori algorithm. Design/methodology/approach Combined with the characteristics of the mobile e-commerce, an improved Apriori algorithm was proposed and applied to the recommendation system. This paper makes products that are recommended to consumers valuable by improving the data mining efficiency. Finally, a Taobao online dress shop is used as an example to prove the effectiveness of an improved Apriori algorithm in the mobile e-commerce recommendation system. Findings The results of the experimental study clearly show that the mobile e-commerce recommendation system based on an improved Apriori algorithm increases the efficiency of data mining to achieve the unity of real time and recommendation accuracy. Originality/value The improved Apriori algorithm is applied in the mobile e-commerce recommendation system solving the limitation of the visual interface in a mobile terminal and the mass data that are continuously generated. The proposed recommendation system provides greater prediction accuracy than conventional systems in data mining.