E4SRec：一种用于序列推荐的优雅、有效、高效且可扩展的大语言模型解决方案
E4SRec: An Elegant Effective Efficient Extensible Solution of Large Language Models for Sequential Recommendation
AI总结：
针对LLM难以建模推荐ID且自然语言生成方案低效、易越界的问题，本文提出E4SRec，以ID序列输入并一次前向生成候选内完整排序，仅训练少量可插拔参数。
摘要：
大语言模型（LLM）的最新进展激发了人们在推荐系统中挖掘其潜力的兴趣。由于LLM是为自然语言任务设计的，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有丰富的语义信息，往往会生成超出范围的结果，并且存在效率显著偏低和可扩展性有限的问题。此外，实用的基于ID的推荐策略依赖大量唯一身份（ID）来表示用户和物品，因其有效性和高效性而在真实推荐系统中受到重视。尽管如此，LLM无法对ID进行建模，这给利用LLM实现个性化推荐带来了严峻挑战。本文提出一种用于序列推荐的优雅、有效、高效且可扩展的大语言模型解决方案E4SRec，它将LLM与仅使用ID表示物品的传统推荐系统无缝集成。具体而言，E4SRec以ID序列作为输入，确保生成的输出落在候选列表内。此外，E4SRec能够在单次前向过程中生成完整排序列表，并且只需要极少的可插拔参数；这些参数针对每个数据集进行训练，同时保持整个LLM冻结。我们通过在四个广泛使用的真实世界数据集上开展综合实验，证实了所提出E4SRec的有效性、高效性和可扩展性。实现代码可在https://github.com/HestiaSky/E4SRec/获取。
英文摘要：
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/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
