Most existing news recommendation methods tackle this task by conducting semantic matching between candidate news and user representation produced by historical clicked news. However, they overlook the high-level connections among different news articles and also ignore the profound relationship between these news articles and users. And the definition of these methods dictates that they can only deliver news articles as-is. On the contrary, integrating several relevant news articles into a coherent narrative would assist users in gaining a quicker and more comprehensive understanding of events. In this paper, we propose a novel generative news recommendation paradigm that includes two steps: (1) Leveraging the internal knowledge and reasoning capabilities of the Large Language Model (LLM) to perform high-level matching between candidate news and user representation; (2) Generating a coherent and logically structured narrative based on the associations between related news and user interests, thus engaging users in further reading of the news. Specifically, we propose GNR to implement the generative news recommendation paradigm. First, we compose the dual-level representation of news and users by leveraging LLM to generate theme-level representations and combine them with semantic-level representations. Next, in order to generate a coherent narrative, we explore the news relation and filter the related news according to the user preference. Finally, we propose a novel training method named UIFT to train the LLM to fuse multiple news articles in a coherent narrative. Extensive experiments show that GNR can improve recommendation accuracy and eventually generate more personalized and factually consistent narratives.

翻译：大多数现有的新闻推荐方法通过进行候选新闻与由用户历史点击新闻生成的用户表示之间的语义匹配来处理此任务。然而，这些方法忽略了不同新闻文章之间的高层联系，也忽视了这些新闻文章与用户之间的深层关系。而且，这些方法的定义决定了它们只能原样传递新闻文章。相反，将几个相关的新闻文章整合成一个连贯的叙事将有助于用户更快、更全面地理解事件。在本文中，我们提出了一种新颖的生成式新闻推荐范式，包括两个步骤：（1）利用大型语言模型（LLM）的内部知识和推理能力，进行候选新闻与用户表示之间的高层匹配；（2）基于相关新闻与用户兴趣之间的关联，生成连贯且逻辑结构清晰的叙事，从而吸引用户进一步阅读新闻。具体来说，我们提出了GNR来实现生成式新闻推荐范式。首先，我们通过利用LLM生成主题级表示，并将其与语义级表示相结合，来构建新闻和用户的双层表示。接着，为了生成连贯的叙事，我们探索新闻关系并根据用户偏好筛选相关新闻。最后，我们提出了一种名为UIFT的新颖训练方法，用于训练LLM将多篇新闻文章融合成一个连贯的叙事。大量实验表明，GNR能够提高推荐准确性，并最终生成更具个性化和事实一致性的叙事。