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.

本文提出了一种名为 PALR 的新框架，旨在将用户历史行为与大型语言模型相结合，生成用户喜欢的商品。实验结果表明，与现有方法相比，我们的解决方案在各种连续推荐任务上表现优异。