Application of an improved Apriori algorithm in a mobile e-commerce recommendation system. Issue 2 (13th March 2017)

• Record Type: Journal Article
• Authors: Guo, Yan; Wang, Minxi; Li, Xin
• Is Part Of: Industrial management & data systems. Volume 117:Issue 2(2017)
• Page Start: 287
• Page End: 303
• Publication Date: 2017-03-13
• Subjects: Data mining -- Improved Apriori algorithm -- Mobile e-commerce -- Recommendation system; Industrial management -- Periodicals; Electronic data processing -- Periodicals; Business -- Periodicals; Industrial management -- Great Britain -- Periodicals; 658.05
• DOI: 10.1108/IMDS-03-2016-0094
• Languages: English
• ISSNs: 0263-5577

Abstract

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.