With the advancement of large language models (LLMs), researchers have explored various methods to optimally leverage their comprehension and generation capabilities in sequential recommendation scenarios. However, several challenges persist in this endeavor. Firstly, most existing approaches rely on the input-output prompting paradigm, which can result in irrelevant or inaccurate responses. Secondly, while there have been attempts to enhance LLMs using prompting strategies such as chain-of-thought (CoT), these efforts have not fully harnessed the reasoning abilities of LLMs or effectively captured the multifaceted information contained within user sequences. To address these limitations, we propose GOT4Rec, a sequential recommendation method that utilizes the graph of thoughts (GoT) prompting strategy. Specifically, we identify and utilize three key types of information within user history sequences: short-term interests, long-term interests and collaborative information from other users. Our approach enables LLMs to independently reason and generate recommendations based on these distinct types of information, subsequently aggregating the results within the GoT framework to derive the final recommended items. This method allows LLMs, with enhanced reasoning capabilities, to more effectively consider the diverse information within user sequences, resulting in more accurate recommendations and more comprehensive explanations. Extensive experiments on real-world datasets demonstrate the effectiveness of GOT4Rec, indicating that it outperforms existing state-of-the-art baselines. Our code is available at https://anonymous.4open.science/r/GOT4Rec-ED99.

翻译：随着大语言模型（LLM）的发展，研究人员已探索了多种方法，以在序列推荐场景中最佳地利用其理解与生成能力。然而，这一努力仍面临若干挑战。首先，现有方法大多依赖于输入-输出提示范式，这可能导致不相关或不准确的响应。其次，尽管已有尝试通过思维链（CoT）等提示策略来增强LLM，但这些工作尚未充分利用LLM的推理能力，亦未能有效捕捉用户序列中包含的多方面信息。为应对这些局限，本文提出GOT4Rec，一种利用思维图（GoT）提示策略的序列推荐方法。具体而言，我们识别并利用用户历史序列中的三种关键信息类型：短期兴趣、长期兴趣以及来自其他用户的协同信息。我们的方法使LLM能够基于这些不同类型的信息独立推理并生成推荐，随后在GoT框架内聚合结果以得出最终推荐项目。该方法使得具备增强推理能力的LLM能够更有效地考量用户序列中的多样化信息，从而产生更准确的推荐和更全面的解释。在真实数据集上的大量实验证明了GOT4Rec的有效性，表明其性能优于现有的先进基线方法。我们的代码发布于 https://anonymous.4open.science/r/GOT4Rec-ED99。