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.

翻译：大语言模型（LLMs）近年来在包括推荐系统在内的多个领域引起了广泛关注。现有研究利用LLMs的能力来提升推荐系统的性能与用户建模效果，这些工作主要集中于运用LLMs解析推荐任务中的文本数据。然而值得注意的是，在基于ID的推荐场景中，通常不存在文本数据，仅能获取ID数据。LLMs在基于ID的推荐范式下处理ID数据的潜力尚未得到充分探索。为此，我们提出了一种开创性方法——“面向ID推荐的大语言模型”（LLM4IDRec）。该创新方法在完全依赖ID数据的前提下融合了LLMs的能力，从而突破了以往对文本数据的依赖。LLM4IDRec的核心思想在于：通过利用LLM对ID数据进行增强，若增强后的ID数据能提升推荐性能，则证明LLM具备有效解析ID数据的能力，这为LLM与基于ID的推荐系统的融合探索了一条创新路径。我们在三个广泛使用的数据集上评估了LLM4IDRec方法的有效性。实验结果表明，仅通过对输入数据进行增强，我们的方法在推荐性能上取得了显著提升，且持续优于现有基于ID的推荐方法。