利用扩展的大型语言模型提升序列推荐系统的性能
Improving the Performance of Sequential Recommendation Systems with an Extended Large Language Model
AI总结：
该研究在LlamaRec框架中将Llama2替换为Llama3，并通过统一随机种子与输入数据进行公平实验，显著提升了序列推荐性能。
摘要：
近期，主要科技公司在人工智能（AI）领域的竞争加剧，新的大型语言模型（LLM）持续发布，并展现出更强的语言理解和基于上下文的推理能力。预计这些进展将通过提升训练数据质量和架构设计，使基于LLM的推荐系统实现更高效的个性化推荐。然而，许多研究尚未考虑这些最新发展。本研究提出通过在LlamaRec框架中以Llama3替换Llama2来改进基于LLM的推荐系统。为确保公平比较，研究设置了随机种子值，并在预处理和训练期间提供相同的输入数据。实验结果显示，在ML-100K、Beauty和Games数据集上，平均性能分别提升38.65%、8.69%和8.19%，从而证实了该方法的实用性。值得注意的是，通过模型替换取得的显著提升表明，无需对系统进行结构性改变，即可以具有成本效益的方式提高推荐质量。基于这些结果，我们认为所提出的方法是提升当前推荐系统性能的一种可行解决方案。
英文摘要：
Recently, competition in the field of artificial intelligence (AI) has intensified among major technological companies, resulting in the continuous release of new large-language models (LLMs) that exhibit improved language understanding and context-based reasoning capabilities. It is expected that these advances will enable more efficient personalized recommendations in LLM-based recommendation systems through improved quality of training data and architectural design. However, many studies have not considered these recent developments. In this study, it was proposed to improve LLM-based recommendation systems by replacing Llama2 with Llama3 in the LlamaRec framework. To ensure a fair comparison, random seed values were set and identical input data was provided during preprocessing and training. The experimental results show average performance improvements of 38.65\%, 8.69\%, and 8.19\% for the ML-100K, Beauty, and Games datasets, respectively, thus confirming the practicality of this method. Notably, the significant improvements achieved by model replacement indicate that the recommendation quality can be improved cost-effectively without the need to make structural changes to the system. Based on these results, it is our contention that the proposed approach is a viable solution for improving the performance of current recommendation systems.
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。
