  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.
