Conventional recommendation methods have achieved notable advancements by harnessing collaborative or sequential information from user behavior. Recently, large language models (LLMs) have gained prominence for their capabilities in understanding and reasoning over textual semantics, and have found utility in various domains, including recommendation. Conventional recommendation methods and LLMs each have their strengths and weaknesses. While conventional methods excel at mining collaborative information and modeling sequential behavior, they struggle with data sparsity and the long-tail problem. LLMs, on the other hand, are proficient at utilizing rich textual contexts but face challenges in mining collaborative or sequential information. Despite their individual successes, there is a significant gap in leveraging their combined potential to enhance recommendation performance. In this paper, we introduce a general and model-agnostic framework known as \textbf{L}arge \textbf{la}nguage model with \textbf{m}utual augmentation and \textbf{a}daptive aggregation for \textbf{Rec}ommendation (\textbf{Llama4Rec}). Llama4Rec synergistically combines conventional and LLM-based recommendation models. Llama4Rec proposes data augmentation and prompt augmentation strategies tailored to enhance the conventional model and LLM respectively. An adaptive aggregation module is adopted to combine the predictions of both kinds of models to refine the final recommendation results. Empirical studies on three real-world datasets validate the superiority of Llama4Rec, demonstrating its consistent outperformance of baseline methods and significant improvements in recommendation performance.

翻译：传统推荐方法通过利用用户行为中的协同或序列信息取得了显著进展。近年来，大型语言模型（LLMs）因其在理解和推理文本语义方面的能力而日益受到关注，并在包括推荐系统在内的多个领域中得到应用。传统推荐方法与LLMs各有优劣：传统方法擅长挖掘协同信息和建模序列行为，但在处理数据稀疏性和长尾问题时存在局限；而LLMs善于利用富文本语境，却在挖掘协同或序列信息方面面临挑战。尽管二者均取得了个体成功，但在充分发挥其协同潜力以提升推荐性能方面仍存在显著差距。本文提出一种通用且模型无关的框架——基于互增强与自适应聚合的大型语言模型推荐系统（Llama4Rec）。Llama4Rec将传统推荐模型与基于LLM的推荐模型进行协同融合，通过专门设计的数据增强和提示增强策略分别提升传统模型与LLM的性能，并采用自适应聚合模块整合两类模型的预测结果以优化最终推荐结果。在三个真实数据集上的实证研究验证了Llama4Rec的优越性，表明其一致优于基线方法，并显著提升了推荐性能。