Online news platforms often use personalized news recommendation methods to help users discover articles that align with their interests. These methods typically predict a matching score between a user and a candidate article to reflect the user's preference for the article. Some previous works have used language model techniques, such as the attention mechanism, to capture users' interests based on their past behaviors, and to understand the content of articles. However, these existing model architectures require adjustments if additional information is taken into account. Pre-trained large language models, which can better capture word relationships and comprehend contexts, have seen a significant development in recent years, and these pre-trained models have the advantages of transfer learning and reducing the training time for downstream tasks. Meanwhile, prompt learning is a newly developed technique that leverages pre-trained language models by building task-specific guidance for output generations. To leverage textual information in news articles, this paper introduces the pre-trained large language model and prompt-learning to the community of news recommendation. The proposed model "prompt-based news recommendation" (PBNR) treats the personalized news recommendation as a text-to-text language task and designs personalized prompts to adapt to the pre-trained language model -- text-to-text transfer transformer (T5). Experimental studies using the Microsoft News dataset show that PBNR is capable of making accurate recommendations by taking into account various lengths of past behaviors of different users. PBNR can also easily adapt to new information without changing the model architecture and the training objective. Additionally, PBNR can make recommendations based on users' specific requirements, allowing human-computer interaction in the news recommendation field.

翻译：在线新闻平台常使用个性化新闻推荐方法帮助用户发现符合其兴趣的文章。这些方法通常预测用户与候选文章之间的匹配分数，以反映用户对文章的偏好。以往一些研究采用语言模型技术（如注意力机制）基于用户历史行为捕捉其兴趣，并理解文章内容。然而，现有模型架构在纳入额外信息时需进行调整。近年来，预训练大语言模型取得显著发展，这类模型能更好捕捉词语间关系并理解上下文，具备迁移学习优势，可减少下游任务的训练时间。与此同时，提示学习作为新兴技术，通过构建任务特定指导来利用预训练语言模型生成输出。为利用新闻文章中的文本信息，本文首次将预训练大语言模型与提示学习引入新闻推荐领域。所提出的"基于提示的新闻推荐"（PBNR）模型将个性化新闻推荐视为文本到文本的语言任务，并设计个性化提示以适应预训练语言模型——文本到文本迁移变换器（T5）。基于微软新闻数据集的实验表明，PBNR能充分考虑不同用户历史行为的长度差异，做出准确推荐。该模型无需改变架构与训练目标即可轻松适应新信息。此外，PBNR能依据用户特定需求进行推荐，实现了新闻推荐领域的人机交互。