用大模型增强仅含ID数据的推荐系统，不依赖文本也能提效。
Enhancing ID-based Recommendation with Large Language Models
- 用大模型对用户/物品ID进行语义增强，生成更丰富的表示。
- 在三个数据集上均超越现有方法，提升推荐效果。
- 适合无文本信息的场景，如隐私保护或数据缺失环境。
大语言模型（LLMs）在推荐系统中备受关注，现有研究多利用其处理文本数据的能力。然而，在仅含用户与物品ID的推荐场景中，缺乏文本数据，而大模型对ID数据的潜力尚未被充分探索。为此，我们提出首个专为ID-based推荐设计的框架LLM4IDRec，仅依赖ID数据，通过大模型对ID进行语义增强，验证其对推荐性能的提升能力。实验基于三个主流数据集，结果表明该方法在不引入额外文本信息的前提下，持续优于现有基线方法，证明了大模型有效理解并利用纯ID数据的可能性。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have recently garnered significant attention in various domains, including recommendation systems. Recent research leverages the capabilities of LLMs to improve the performance and user modeling aspects of recommender systems. These studies primarily focus on utilizing LLMs to interpret textual data in recommendation tasks. However, it's worth noting that in ID-based recommendations, textual data is absent, and only ID data is available. The untapped potential of LLMs for ID data within the ID-based recommendation paradigm remains relatively unexplored. To this end, we introduce a pioneering approach called "LLM for ID-based Recommendation" (LLM4IDRec). This innovative approach integrates the capabilities of LLMs while exclusively relying on ID data, thus diverging from the previous reliance on textual data. The basic idea of LLM4IDRec is that by employing LLM to augment ID data, if augmented ID data can improve recommendation performance, it demonstrates the ability of LLM to interpret ID data effectively, exploring an innovative way for the integration of LLM in ID-based recommendation. We evaluate the effectiveness of our LLM4IDRec approach using three widely-used datasets. Our results demonstrate a notable improvement in recommendation performance, with our approach consistently outperforming existing methods in ID-based recommendation by solely augmenting input data.
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