Analysis of Intelligent Recommendation Systems and Consumer Behavior Theories on E-Commerce Platforms
Xie Zunying · Philosophy and social science. · 2024
This study explores the interplay between intelligent recommendation systems and consumer behavior theories on e-commerce platforms. With the rapid growth of e-commerce, intelligent recommendation systems have become vital tools for enhancing user experience and boosting sales. While much literature addresses the technical implementation and algorithm optimization of these systems, research from the perspective of consumer behavior theory is limited. This paper first reviews the fundamental principles and technological evolution of recommendation systems, summarizing common algorithms and their specific applications in e-commerce. Next, from the viewpoint of consumer behavior theory, it systematically analyzes the impact of recommendation systems on consumer decision-making processes, purchasing behavior, and user satisfaction. It examines how recommendation systems influence consumer decisions and purchase intentions through mechanisms such as information overload, choice simplification, social recognition, and a sense of belonging. Additionally, the paper evaluates the applicability and effectiveness of various types of recommendation systems (e. g., personalized, contextual, and social recommendations) in different consumption scenarios. Findings indicate that intelligent recommendation systems significantly enhance shopping experiences and satisfaction while profoundly affecting purchasing decisions. This research provides theoretical guidance for designing recommendation systems on e-commerce platforms and offers new perspectives and methods for future consumer behavior studies. By delving into the complex interactions between recommendation systems and consumer behavior, it provides valuable insights for the intelligent transformation and user experience optimization of e-commerce platforms.