Generative News Recommendation

ACM Web Conference (WWW), pp. 3444–3453

Abstract

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.

Authors 7

1. Shen Gao

   University of Electronic Science and Technology of China

   Affiliation as printed

   University of Electronic Science and Technology of China, Chengdu, China

2. Jiabao Fang

   Shandong University

   Affiliation as printed

   Shandong University, Qingdao, China

3. Quan Tu

   Renmin University of China

   Affiliation as printed

   Renmin University of China, Beijing, China

4. Zhitao Yao

   Shandong University

   Affiliation as printed

   Shandong University, Qingdao, China

5. Zhumin Chen

   Shandong University

   Affiliation as printed

   Shandong University, Qingdao, China

6. Pengjie Ren

   Shandong University

   Affiliation as printed

   Shandong University, Qingdao, China

7. Zhaochun Ren Aachen

   Leiden University

   Affiliation as printed

   Leiden University, Leiden, Netherlands