AI驱动的智能推荐对消费者购买决策的影响机制研究

The Mechanisms of AI-Driven Intelligent Recommendation on Consumer Purchase Decision-Making

摘要: 在人工智能技术广泛应用于电子商务平台的背景下，智能推荐系统已成为影响消费者购买决策的重要机制。本文基于质性分析视角，围绕AI驱动的智能推荐如何影响消费者购买决策这一核心问题，系统梳理相关研究，并结合平台实践情境，对其作用机制与现实困境进行深入分析。研究发现，智能推荐通过信息筛选、路径依赖与情境嵌入等方式，在提升信息匹配效率的同时，也可能引发信息茧房、算法不透明、决策依赖与情境诱导等问题，从而对消费者决策质量产生结构性影响。在此基础上，本文从提升算法透明度、引入多样化机制、强化用户控制权及建立行为干预机制等方面提出优化路径。本文从质性视角丰富了智能推荐与消费者行为研究，并为平台算法治理与消费者权益保护提供了理论参考。

Abstract: In the context of the widespread application of artificial intelligence technologies in e-commerce platforms, intelligent recommendation systems have become a critical mechanism influencing consumer purchase decision-making. From a qualitative perspective, this study focuses on the core question of how AI-driven intelligent recommendations affect consumer purchasing decisions. By systematically reviewing the existing literature and incorporating practical platform contexts, this paper analyzes the underlying mechanisms and real-world challenges associated with intelligent recommendation systems. The findings indicate that intelligent recommendation systems, through information filtering, path dependence, and contextual embedding, not only enhance information matching efficiency but also give rise to issues such as information cocoons, algorithmic opacity, decision dependence, and contextual inducement. These factors, in turn, exert a structural influence on the quality of consumer decision-making. Based on these insights, this study proposes several optimization strategies, including improving algorithm transparency, introducing diversity mechanisms, strengthening user control, and establishing behavioral intervention mechanisms. This research enriches the literature on intelligent recommendation and consumer behavior from a qualitative perspective and provides theoretical implications for platform algorithm governance and consumer protection.