9 papers
HiSkill: Empowering LLM Agents with Hierarchical Skill Graphs
Yu Hao, Jinxuan Cai, Qi Zhang +4
Skills have become an important abstraction for enabling large language model (LLM) agents to reuse past experience in long-horizon interactive tasks. However, existing trajectory-…
Generative Data Transformation: From Mixed to Unified Data
Jiaqing Zhang, Mingjia Yin, Hao Wang +6
Recommendation model performance is intrinsically tied to the quality, volume, and relevance of their training data. To address common challenges like data sparsity and cold start,…
GNNVerifier: Graph-based Verifier for LLM Task Planning
Yu Hao, Qiuyu Wang, Cheng Yang +3
Large language models (LLMs) facilitate the development of autonomous agents. As a core component of such agents, task planning aims to decompose complex natural language requests…
Efficient Personalized Reranking with Semi-Autoregressive Generation and Online Knowledge Distillation
Kai Cheng, Hao Wang, Wei Guo +4
Generative models offer a promising paradigm for the final stage reranking in multi-stage recommender systems, with the ability to capture inter-item dependencies within reranked l…
CiteLLM: An Agentic Platform for Trustworthy Scientific Reference Discovery
Mengze Hong, Di Jiang, Chen Jason Zhang +5
Large language models (LLMs) have created new opportunities to enhance the efficiency of scholarly activities; however, challenges persist in the ethical deployment of AI assistanc…
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…
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…
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…
Graph Foundation Models for Recommendation: A Comprehensive Survey
Bin Wu, Yihang Wang, Yuanhao Zeng +7
Recommender systems (RS) serve as a fundamental tool for navigating the vast expanse of online information, with deep learning advancements playing an increasingly important role i…