no code implementations • 9 Apr 2024 • Shen Gao, Yifan Wang, Jiabao Fang, Lisi Chen, Peng Han, Shuo Shang
Recommendation systems play a crucial role in various domains, suggesting items based on user behavior. However, the lack of transparency in presenting recommendations can lead to user confusion.
1 code implementation • 6 Mar 2024 • Shen Gao, Jiabao Fang, Quan Tu, Zhitao Yao, Zhumin Chen, Pengjie Ren, Zhaochun Ren
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.
no code implementations • 2 Feb 2024 • Jiabao Fang, Shen Gao, Pengjie Ren, Xiuying Chen, Suzan Verberne, Zhaochun Ren
First, we design a multi-agent act planning framework, which can control the dialogue flow based on four LLM-based agents.