Table of Content

Open Access iconOpen Access

ARTICLE

crossmark

Personalised Product Recommendation Model Based on User Interest

Jitao Zhang

Jiangxi University of technology

* Corresponding Author: Email: email

Computer Systems Science and Engineering 2019, 34(4), 231-236. https://doi.org/10.32604/csse.2019.34.231

Abstract

The scale of e-commerce systems is increasing and more and more products are being offered online. However, users must find their own desired products among a large amount of unrelated information, which makes it increasingly difficult for them to make a purchase. In order to solve this problem of information overload, and effectively assist e-commerce users to shop easily and conveniently, an e-commerce personalized recommendation system technology has been proposed. This paper introduces the design and implementation of a personalized product recommendation model based on user interest. The “shopping basket analysis” functional model centered on the Apriori algorithm uses the sales data in the transaction database to mine various interesting links between the products purchased by the customers. Moreover, it helps merchants to formulate marketing strategies, reasonably arranges shelf-guided sales, and attracts more customers. This platform adopts a B/S structure and uses JSP+AJAX technology to achieve the dynamic loading of pages. In the background, the Struts2 framework is combined with the SQL Server database to establish the system function module, and then the function is gradually improved according to the requirement analysis, and the development of the platform is finally completed.

Keywords


Cite This Article

J. Zhang, "Personalised product recommendation model based on user interest," Computer Systems Science and Engineering, vol. 34, no.4, pp. 231–236, 2019.

Citations




cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 1437

    View

  • 1018

    Download

  • 1

    Like

Share Link