arXiv 2501.021782025-01-17cs.IR

大语言模型在推荐系统中的应用

The Application of Large Language Models in Recommendation Systems

Peiyang Yu, Zeqiu Xu, Jiani Wang, Xiaochuan Xu

AI总结：

该研究探讨大语言模型在推荐系统中的应用，指出其可利用非结构化数据克服传统推荐方法的局限，提升推荐效果与系统适配性，同时分析了落地挑战及行业创新潜力。

摘要：

将大语言模型（Large Language Models, LLM）整合到推荐框架中，对个性化体验及适配用户需求具有关键优势。传统推荐方法（如协同过滤、基于内容的过滤）在解决冷启动问题、数据稀疏性及考虑的信息多样性不足方面存在严重局限。以GPT-4为代表的LLM已成为强大工具，使推荐框架能够利用用户评论、社交互动、文本类内容等非结构化数据源。通过分析这些数据源，LLM提升了推荐的准确性和相关性，从而克服了传统方法的部分局限。本研究探讨了LLM在推荐系统中的应用，重点覆盖电子商务、社交媒体平台、流媒体服务及教育技术领域，展示了LLM如何丰富推荐多样性、提升用户参与度及系统适配性，同时也探究了其技术落地相关的挑战。本研究也可视为一项展现LLM变革用户体验、推动行业创新潜力的研究。

英文摘要：

The integration of Large Language Models into recommendation frameworks presents key advantages for personalization and adaptability of experiences to the users. Classic methods of recommendations, such as collaborative filtering and content-based filtering, are seriously limited in the solution of cold-start problems, sparsity of data, and lack of diversity in information considered. LLMs, of which GPT-4 is a good example, have emerged as powerful tools that enable recommendation frameworks to tap into unstructured data sources such as user reviews, social interactions, and text-based content. By analyzing these data sources, LLMs improve the accuracy and relevance of recommendations, thereby overcoming some of the limitations of traditional approaches. This work discusses applications of LLMs in recommendation systems, especially in electronic commerce, social media platforms, streaming services, and educational technologies. This showcases how LLMs enrich recommendation diversity, user engagement, and the system's adaptability; yet it also looks into the challenges connected to their technical implementation. This can also be presented as a study that shows the potential of LLMs for changing user experiences and making innovation possible in industries.

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