PALR：面向推荐的个性化感知大语言模型
PALR: Personalization Aware LLMs for Recommendation
AI总结：
本文提出PALR框架，结合用户历史行为、候选检索与微调的70亿参数LLM排序模型，在多种序列推荐任务上超过现有最先进方法。
摘要：
大语言模型（LLM）近期因其卓越能力而受到广泛关注。尽管人们已投入大量努力开发可用于各类自然语言处理（NLP）任务的通用LLM，但探索其在推荐系统中潜力的研究仍然较少。本文提出一种名为PALR的新颖框架，旨在将用户历史行为（如点击、购买、评分等）与LLM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LLM的排序模型生成推荐物品。现有方法通常使用通用LLM进行零样本/少样本推荐测试，或在参数量不足10亿的小型语言模型上训练，无法充分激发LLM的推理能力，也难以利用丰富的物品侧参数化知识；与之不同，我们为排序任务微调了一个具有70亿参数的LLM。该模型将自然语言格式的检索候选作为输入，并在推理时通过指令明确要求从输入候选中选择结果。实验结果表明，我们的方案在多种序列推荐任务上优于最先进模型。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
