AI Recommendation Algorithms and E-commerce Purchasing Behavior: A Value Chain Perspective

Abstract

: With the rapid development of artificial intelligence technologies, recommendation algorithms have become a crucial tool for e-commerce platforms to enhance user experience and improve conversion rates. Based on value chain theory, this paper systematically examines the influence mechanisms of AI recommendation algorithms on consumer purchasing behavior in e-commerce, focusing on key stages such as user acquisition, information matching, decision support, and post-purchase feedback.By reviewing relevant literature and constructing a theoretical model, the study explores the functional pathways of recommendation algorithms across different stages of the value chain and their combined effects on consumer cognition, emotions, and behavioral decisions. The findings indicate that recommendation algorithms significantly promote purchase intention and actual purchasing behavior by improving information matching efficiency, enhancing user experience, and reducing decision-making costs. Meanwhile, trust mechanisms and perceived value play mediating roles in this process. This study provides both theoretical support and practical insights for e-commerce platforms to optimize recommendation strategies and improve user conversion rates.