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Found 3 papers, 1 papers with code
DRE: Generating Recommendation Explanations by Aligning Large Language Models at Data-level
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.
Generative News Recommendation
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.
A Multi-Agent Conversational Recommender System
First, we design a multi-agent act planning framework, which can control the dialogue flow based on four LLM-based agents.
