Large Language Models (LLMs) have achieved remarkable success in recent years, owing to their impressive generalization capabilities and rich world knowledge. To capitalize on the potential of using LLMs as recommender systems, mainstream approaches typically focus on two paradigms. The first paradigm designs multi-domain or multi-task instruction data for generalizable recommendation, so as to align LLMs with general recommendation areas and deal with cold-start recommendation. The second paradigm focuses on enhancing domain-specific recommendation tasks, improving performance in warm recommendation scenarios. While most previous works treat these two paradigms separately, we argue that they have complementary advantages, and combining them can yield better results. In this paper, we propose a generalizable and efficient LLM-based recommendation framework RecCocktail. Our approach begins with fine-tuning a "base spirit" LoRA module using domain-general recommendation instruction data to align LLM with recommendation knowledge. Next, given users' behavior of a specific domain, we construct a domain-specific "ingredient" LoRA module. We then provide an entropy-guided adaptive merging method to mix the "base spirit" and the "ingredient" in the weight space. Please note that, RecCocktail combines the advantages of the existing two paradigms without introducing additional time or space overhead during the inference phase. Moreover, RecCocktail is efficient with plug and play, as the "base spirit" LoRA is trained only once, and any domain-specific "ingredient" can be efficiently mixed with only domain-specific fine-tuning. Extensive experiments on multiple datasets under both warm and cold-start recommendation scenarios validate the effectiveness and generality of the proposed RecCocktail.

翻译：近年来，大语言模型凭借其卓越的泛化能力和丰富的世界知识取得了显著成功。为利用大语言模型作为推荐系统的潜力，主流方法通常聚焦于两种范式。第一种范式设计多领域或多任务指令数据以实现通用推荐，旨在使大语言模型与通用推荐领域对齐并处理冷启动推荐问题。第二种范式专注于增强特定领域的推荐任务，以提升热启动推荐场景下的性能。尽管先前研究大多将这两种范式分开处理，我们认为它们具有互补优势，结合二者可获得更好效果。本文提出一种通用且高效的大语言模型推荐框架RecCocktail。我们的方法首先使用领域通用推荐指令数据微调一个“基酒”LoRA模块，使大语言模型与推荐知识对齐。接着，根据用户在特定领域的行为数据，我们构建一个领域特定的“配料”LoRA模块。随后，我们提出一种基于熵引导的自适应融合方法，在权重空间中对“基酒”与“配料”进行混合。需要指出的是，RecCocktail在推理阶段不引入额外时间或空间开销的前提下，综合了现有两种范式的优势。此外，RecCocktail具备即插即用的高效性：“基酒”LoRA仅需训练一次，任何领域特定的“配料”仅需通过领域特定微调即可高效融合。在热启动与冷启动推荐场景下对多个数据集进行的广泛实验验证了所提RecCocktail框架的有效性与通用性。