用Llama3替换Llama2，轻松提升推荐系统性能。
Improving the Performance of Sequential Recommendation Systems with an Extended Large Language Model
- 用Llama3替代Llama2，不改结构提升推荐效果。
- 在ML-100K、Beauty、Games上分别提升38.65%、8.69%、8.19%。
- 无需改动架构，低成本提升推荐质量，适合工程优化场景。
近年来，人工智能领域竞争加剧，大型语言模型（LLM）持续迭代，展现出更强的语言理解与上下文推理能力。这些进展有望通过更优的训练数据和架构设计，提升基于LLM的推荐系统性能。然而，许多研究尚未跟进。本文在LlamaRec框架中将Llama2替换为Llama3，保持随机种子与输入数据一致以确保公平对比。实验结果显示，在ML-100K、Beauty、Games数据集上平均性能分别提升38.65%、8.69%、8.19%，验证了该方法的实用性。显著提升表明，仅通过模型替换即可高效改善推荐质量，无需进行结构修改。因此，该方案是当前推荐系统性能优化的可行路径。
原文摘要 · Abstract (English)
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.
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