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已取得显著进展。近期，大语言模型因其在文本语义理解与推理方面的能力而备受关注，并在包括推荐系统在内的多个领域得到应用。传统推荐方法与大语言模型各有优劣：传统方法擅长挖掘协同信息与建模序列行为，但面临数据稀疏性与长尾问题；大语言模型则善于利用丰富文本语境，却在挖掘协同或序列信息方面存在局限。尽管各自取得成功，如何协同利用两者潜力以提升推荐性能仍存在显著空白。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语言模型推荐系统（Llama4Rec）。该框架通过协同整合传统推荐模型与大语言模型，分别设计了针对传统模型的数据增强策略与面向大语言模型的提示增强策略，并采用自适应聚合模块融合两类模型的预测结果以优化最终推荐。在三个真实数据集上的实证研究验证了Llama4Rec的优越性，其不仅持续超越基线方法，更在推荐性能上实现了显著提升。