EconPapers    
Economics at your fingertips  
 

An E-Commerce Recommendation System Based on Dynamic Analysis of Customer Behavior

Farah Tawfiq Abdul Hussien, Abdul Monem S. Rahma and Hala B. Abdulwahab
Additional contact information
Farah Tawfiq Abdul Hussien: Computer Science Department, University of Technology, Baghdad 10001, Iraq
Abdul Monem S. Rahma: Computer Science Department, University of Technology, Baghdad 10001, Iraq
Hala B. Abdulwahab: Computer Science Department, University of Technology, Baghdad 10001, Iraq

Sustainability, 2021, vol. 13, issue 19, 1-21

Abstract: The technological development in the devices and services provided via the Internet and the availability of modern devices and their advanced applications, for most people, have led to an increase in the expansion and a trend towards electronic commerce. The large number and variety of goods offered on e-commerce websites sometimes make the customers feel overwhelmed and sometimes make it difficult to find the right product. These factors increase the amount of competition between global commercial sites, which increases the need to work efficiently to increase financial profits. The recommendation systems aim to improve the e-commerce systems performance by facilitating the customers to find the appropriate products according to their preferences. There are lots of recommendation system algorithms that are implemented for this purpose. However, most of these algorithms suffer from several problems, including: cold start, sparsity of user-item matrix, scalability, and changes in user interest. This paper aims to develop a recommendation system to solve the problems mentioned before and to achieve high realistic prediction results this is done by building the system based on the customers’ behavior and cooperating with the statistical analysis to support decision making, to be employed on an e-commerce site and increasing its performance. The project contribution can be shown by the experimental results using precision, recall, F-function, mean absolute error (MAE), and root mean square error (RMSE) metrics, which are used to evaluate system performance. The experimental results showed that using statistical methods improves the decision-making that is employed to increase the accuracy of recommendation lists suggested to the customers.

Keywords: customized recommendation system; customer behavior; product features; customer preference matrix; product feature matrix (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/13/19/10786/pdf (application/pdf)
https://www.mdpi.com/2071-1050/13/19/10786/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:13:y:2021:i:19:p:10786-:d:645440

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager (indexing@mdpi.com).

 
Page updated 2024-12-28
Handle: RePEc:gam:jsusta:v:13:y:2021:i:19:p:10786-:d:645440