综述大模型如何革新生成式推荐系统
GR-LLMs: Recent Advances in Generative Recommendation Based on Large Language Models
- 梳理LLM在生成式推荐中的核心应用与技术路径
- 总结工业场景落地的关键挑战与应对策略
- 指明未来研究方向，适合推荐系统研究者参考
过去一年，生成式推荐（GR）取得了显著进展，尤其体现在利用大型语言模型（LLM）强大的序列建模和推理能力来提升推荐性能。基于LLM的生成式推荐正形成一种与判别式推荐截然不同的新范式，展现出替代依赖复杂手工特征的传统推荐系统的巨大潜力。本文提供了一次全面的综述，旨在促进基于LLM的生成式推荐领域的进一步研究。首先，我们概述了基于LLM的生成式推荐的基本前提与应用案例；其次，介绍了在实际工业场景中应用基于LLM的生成式推荐时的主要考量；最后，探讨了该领域有前景的研究方向。我们希望本综述能推动生成式推荐领域的持续发展。
原文摘要 · Abstract (English)
In the past year, Generative Recommendations (GRs) have undergone substantial advancements, especially in leveraging the powerful sequence modeling and reasoning capabilities of Large Language Models (LLMs) to enhance overall recommendation performance. LLM-based GRs are forming a new paradigm that is distinctly different from discriminative recommendations, showing strong potential to replace traditional recommendation systems heavily dependent on complex hand-crafted features. In this paper, we provide a comprehensive survey aimed at facilitating further research of LLM-based GRs. Initially, we outline the general preliminaries and application cases of LLM-based GRs. Subsequently, we introduce the main considerations when LLM-based GRs are applied in real industrial scenarios. Finally, we explore promising directions for LLM-based GRs. We hope that this survey contributes to the ongoing advancement of the GR domain.
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