3 papers
cs.CL2025
Thought-Augmented Planning for LLM-Powered Interactive Recommender Agent
Haocheng Yu, Yaxiong Wu, Hao Wang +6
Interactive recommendation is a typical information-seeking task that allows users to interactively express their needs through natural language and obtain personalized recommendat…
cs.IR2025
CORONA: A Coarse-to-Fine Framework for Graph-based Recommendation with Large Language Models
Junze Chen, Xinjie Yang, Cheng Yang +4
Recommender systems (RSs) are designed to retrieve candidate items a user might be interested in from a large pool. A common approach is using graph neural networks (GNNs) to captu…
cs.CL2025
GraphLAMA: Enabling Efficient Adaptation of Graph Language Models with Limited Annotations
Junze Chen, Cheng Yang, Shujie Li +4
Large language models (LLMs) have demonstrated their strong capabilities in various domains, and have been recently integrated for graph analysis as graph language models (GLMs). W…