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

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.

Analysis of Consumer Recommendation Behavior and Market Equilibrium in E-Commerce from the Perspective of Social Media

“…A typical example of social commerce in China is commerce on WeChat, which allows individuals to sell products to friends. This integration of social media and e-commerce has the advantages of large traffic, recommendation by acquaintances, and economic benefits of group buying [10].…”

Enhancing E-Commerce with Big Data: From Browsing to Buying Through Recommendation Systems

“…The integration of the Behavioral Recommender System using Machine Learning (BRS-ML) resonates with Alagarsamy et al (2023), emphasizing the importance of advanced recommendation algorithms for enhancing user engagement. The substantial 33.09% increase in purchase transactions underscores the potential of hybrid recommendation architectures in driving conversions, aligning with Hussien et al (2021), who focused on improving recommendation accuracy for better outcomes. The synthesis of the cited research studies highlights the central role that recommendation systems play in influencing consumer behavior and decision-making within the e-commerce industry.…”

“…These recommendations are borne out of a deep learning process that imbibes insights from previous consumer comments and reviews, thereby aiding customers in unearthing novel products and navigating purchase decisions (Alabdulrahman et al, 2020). These investigations also underscore the contemporary focus on personalized recommendation paradigms founded on consumer behavior and propelled by intelligent algorithms, culminating in an augmented decision-making trajectory (Hussien et al, 2021). The proposition of a consumer-centric multi-party matching recommendation system, rooted in deep learning and attuned to individual consumer attributes, further emphasizes the indispensability of a nuanced understanding of consumer characteristics.…”

Business Management in the Information Age: Use of Systems, Data Processing and Scalability for Organizational Efficiency

“…The ability of companies to adapt to changes in the market and digitalization has generated challenges and opportunities, allowing, for example, inter-business relationships and new forms of cooperation that for [63] give rise to new product and services offerings. For [64] technological development in devices and services provided through the Internet and the availability of modern devices and their advanced applications have caused an increase in expansion and according to [37], [49], [50] a trend towards electronic commerce.…”