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.

Recently, large language models (LLMs) have exhibited significant progress in language understanding and generation. By leveraging textual features, customized LLMs are also applied for recommendation and demonstrate improvements across diverse recommendation scenarios. Yet the majority of existing methods perform training-free recommendation that heavily relies on pretrained knowledge (e.g., movie recommendation). In addition, inference on LLMs is slow due to autoregressive generation, rendering existing methods less effective for real-time recommendation. As such, we propose a two-stage framework using large language models for ranking-based recommendation (LlamaRec). In particular, we use small-scale sequential recommenders to retrieve candidates based on the user interaction history. Then, both history and retrieved items are fed to the LLM in text via a carefully designed prompt template. Instead of generating next-item titles, we adopt a verbalizer-based approach that transforms output logits into probability distributions over the candidate items. Therefore, the proposed LlamaRec can efficiently rank items without generating long text. To validate the effectiveness of the proposed framework, we compare against state-of-the-art baseline methods on benchmark datasets. Our experimental results demonstrate the performance of LlamaRec, which consistently achieves superior performance in both recommendation performance and efficiency.