Large language models (LLMs) have recently received significant attention for their exceptional capabilities. Despite extensive efforts in developing general-purpose LLMs that can be utilized in various natural language processing (NLP) tasks, there has been less research exploring their potential in recommender systems. In this paper, we propose a novel framework, named PALR, which aiming to combine user history behaviors (such as clicks, purchases, ratings, etc.) with LLMs to generate user preferred items. Specifically, we first use user/item interactions as guidance for candidate retrieval. Then we adopt a LLM-based ranking model to generate recommended items. Unlike existing approaches that typically adopt general-purpose LLMs for zero/few-shot recommendation testing or training on small-sized language models (with less than 1 billion parameters), which cannot fully elicit LLMs' reasoning abilities and leverage rich item side parametric knowledge, we fine-tune a 7 billion parameters LLM for the ranking purpose. This model takes retrieval candidates in natural language format as input, with instruction which explicitly asking to select results from input candidates during inference. Our experimental results demonstrate that our solution outperforms state-of-the-art models on various sequential recommendation tasks.

翻译：大语言模型（LLMs）近期因其卓越能力获得了广泛关注。尽管在开发可用于各类自然语言处理（NLP）任务的通用型大语言模型方面已有大量研究，但探索其在推荐系统中潜力的工作仍相对较少。本文提出一个名为PALR的新框架，旨在将用户历史行为（如点击、购买、评分等）与大语言模型相结合，以生成用户偏好的物品。具体而言，我们首先利用用户/物品交互作为候选检索的引导。随后采用基于大语言模型的排序模型生成推荐物品。不同于现有方法（通常采用通用型大语言模型进行零样本/小样本推荐测试，或在小规模参数量低于10亿的语言模型上进行训练），这些方法无法充分激发大语言模型的推理能力并利用丰富的物品侧参数化知识，我们微调了一个拥有70亿参数的大语言模型用于排序任务。该模型以自然语言格式的检索候选集作为输入，并附带明确指示在推理过程中从输入候选中选择结果的指令。实验结果表明，我们的解决方案在多项序列推荐任务上优于当前最优模型。