[论文解读] Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyond

Qi Wang, Jindong Li+8 more|arXiv (Cornell University)|Oct 10, 2024

Semantic Web and Ontologies被引用 9

一句话总结

本文提出了一个针对基于大模型的推荐系统的三层次分类法—— Representing and Understanding、Scheming and Utilizing、Industrial Deploying——并讨论将 LLMs 融入推荐系统的开发、挑战与行业差距。

ABSTRACT

Large language models (LLMs) have not only revolutionized the field of natural language processing (NLP) but also have the potential to bring a paradigm shift in many other fields due to their remarkable abilities of language understanding, as well as impressive generalization capabilities and reasoning skills. As a result, recent studies have actively attempted to harness the power of LLMs to improve recommender systems, and it is imperative to thoroughly review the recent advances and challenges of LLM-based recommender systems. Unlike existing work, this survey does not merely analyze the classifications of LLM-based recommendation systems according to the technical framework of LLMs. Instead, it investigates how LLMs can better serve recommendation tasks from the perspective of the recommender system community, thus enhancing the integration of large language models into the research of recommender system and its practical application. In addition, the long-standing gap between academic research and industrial applications related to recommender systems has not been well discussed, especially in the era of large language models. In this review, we introduce a novel taxonomy that originates from the intrinsic essence of recommendation, delving into the application of large language model-based recommendation systems and their industrial implementation. Specifically, we propose a three-tier structure that more accurately reflects the developmental progression of recommendation systems from research to practical implementation, including representing and understanding, scheming and utilizing, and industrial deployment. Furthermore, we discuss critical challenges and opportunities in this emerging field. A more up-to-date version of the papers is maintained at: https://github.com/jindongli-Ai/Next-Generation-LLM-based-Recommender-Systems-Survey.

研究动机与目标

• 从推荐系统社区的视角解释 LLMs 如何提升推荐系统。
• 提出一个三层次分类法，以组织基于 LLM 的推荐研究与实践。
• 讨论挑战与机遇，包括学术研究与工业部署之间的差距。

提出的方法

• 评审现有文献并综合出一个超越以 NLP 为中心的分类法的统一视角。
• 为基于 LLM 的推荐系统引入一个正式的记号框架。
• 定义一个内在的三层次分类法，并用具有代表性的工作来说明其组成部分。

Figure 1. An overview of the proposed three-tier taxonomy for LLM-based recommender systems: (a) representing and understanding, (b) scheming and utilizing, and (c) industrial deploying.

实验结果

研究问题

• RQ1 从推荐系统社区的视角，LLMs 如何改进推荐任务？
• RQ2 有一个全面的分类法可以涵盖 LLM-based recommender systems 从研究到工业部署的发展吗？
• RQ3 在桥接学术研究与工业应用方面，关键挑战与机遇是什么？

主要发现

• 提出了一个三层次分类法，用于表示、利用和部署基于 LLM 的推荐系统。
• 本次综述突出了 LLM-Recs 领域中学术研究与工业部署之间的差异。
• 本文讨论了在表示、scheming、和部署中的挑战与机遇（基于 LLM 的推荐系统）。

Figure 3. The general pipeline of LLM-based recommender systems.