The recent advancements in Large Language Models (LLMs) have sparked interest in harnessing their potential within recommender systems. Since LLMs are designed for natural language tasks, existing recommendation approaches have predominantly transformed recommendation tasks into open-domain natural language generation tasks. However, this approach necessitates items to possess rich semantic information, often generates out-of-range results, and suffers from notably low efficiency and limited extensibility. Furthermore, practical ID-based recommendation strategies, reliant on a huge number of unique identities (IDs) to represent users and items, have gained prominence in real-world recommender systems due to their effectiveness and efficiency. Nevertheless, the incapacity of LLMs to model IDs presents a formidable challenge when seeking to leverage LLMs for personalized recommendations. In this paper, we introduce an Elegant Effective Efficient Extensible solution for large language models for Sequential Recommendation (E4SRec), which seamlessly integrates LLMs with traditional recommender systems that exclusively utilize IDs to represent items. Specifically, E4SRec takes ID sequences as inputs, ensuring that the generated outputs fall within the candidate lists. Furthermore, E4SRec possesses the capability to generate the entire ranking list in a single forward process, and demands only a minimal set of pluggable parameters, which are trained for each dataset while keeping the entire LLM frozen. We substantiate the effectiveness, efficiency, and extensibility of our proposed E4SRec through comprehensive experiments conducted on four widely-used real-world datasets. The implementation code is accessible at https://github.com/HestiaSky/E4SRec/.

翻译：近年来，大语言模型（LLMs）的进展激发了人们将其应用于推荐系统的兴趣。由于LLMs专为自然语言任务设计，现有推荐方法主要将推荐任务转化为开放域自然语言生成任务。然而，这种方法要求物品具备丰富的语义信息，常生成超出候选范围的结果，且存在效率显著低下、可扩展性受限等问题。此外，依赖大量唯一标识（ID）表示用户与物品的实用化ID推荐策略，因其实效性与高效性在真实推荐系统中占据主导地位。然而，LLMs对ID建模能力的缺失，为利用其进行个性化推荐带来了严峻挑战。本文提出一种优雅、高效、可扩展的大语言模型序贯推荐解决方案（E4SRec），该方案将LLMs与传统仅使用ID表示物品的推荐系统无缝融合。具体而言，E4SRec以ID序列为输入，确保生成结果始终位于候选列表内。此外，E4SRec具备单次前向过程生成完整排序列表的能力，且仅需训练极少量可插拔参数，同时保持整个LLM冻结。通过在四个广泛使用的真实数据集上开展全面实验，我们验证了所提E4SRec方法在有效性、效率与可扩展性方面的优势。实现代码见https://github.com/HestiaSky/E4SRec/。