基于人工智能大模型能力的电商推荐算法偏向机制与消费者权益保障研究

A Study on Bias Mechanisms in E-Commerce Recommendation Algorithms Based on Large AI Model Capabilities and the Protection of Consumer Rights

摘要: 大语言模型(LLM)的快速迭代正在深刻重塑电商推荐系统的技术路径与运行逻辑。LLM将推荐任务从“用户–物品”协同匹配升级为基于内容理解与语义推理的生成式推荐，显著提升了推荐的个性化水平与交互体验。然而，在零样本学习与冷启动场景中，LLM对预训练阶段嵌入的社会偏见不加过滤地继承和放大，形成了隐蔽而系统性的算法偏向机制。文章从技术机理与市场应用双重维度，系统剖析电商推荐中LLM算法偏向的生成根源、实现路径与表现形式，进而探讨其对消费者知情权、选择权、公平交易权及个人数据安全的侵害机理。在此基础上，梳理我国在算法推荐管理、平台价格行为与用户权益保护等领域的最新立法进展，提出构建技术约束、规则约束、权利约束三重防线协同的治理框架，以期在人工智能(Artificial Intelligence, AI)大模型赋能电商推荐与消费者权益保障之间寻找动态平衡。

Abstract: The rapid evolution of large language models (LLMs) is profoundly reshaping the technical approach and operational logic of e-commerce recommendation systems. LLMs have elevated recommendation tasks from “user - item” collaborative matching to generative recommendations based on content understanding and semantic reasoning, significantly enhancing the level of personalization and the interaction experience. However, in zero-shot learning and cold-start scenarios, LLMs uncritically inherit and amplify the social biases embedded during the pre-training phase, creating a hidden yet systematic algorithmic bias mechanism. This paper systematically analyzes the origins, implementation pathways, and manifestations of algorithmic bias in e-commerce recommendations using LLMs from both technical and market application perspectives. It further explores the mechanisms through which such bias infringes upon consumers’ rights to information, choice, fair transactions, and personal data security. Building on this analysis, the paper reviews the latest legislative developments in China regarding algorithmic recommendation management, platform pricing practices, and user rights protection. It proposes a governance framework that coordinates three lines of defense—technical constraints, regulatory constraints, and rights-based constraints—to strike a dynamic balance between leveraging large AI models to empower e-commerce recommendations and safeguarding consumer rights.