3 papers
cs.LG2026
ReCast: Recasting Learning Signals for Reinforcement Learning in Generative Recommendation
Peiyan Zhang, Hanmo Liu, Chengxuan Tong +3
Generic group-based RL assumes that sampled rollout groups are already usable learning signals. We show that this assumption breaks down in sparse-hit generative recommendation, wh…
cs.CL2025
HeTGB: A Comprehensive Benchmark for Heterophilic Text-Attributed Graphs
Shujie Li, Yuxia Wu, Chuan Shi +1
Graph neural networks (GNNs) have demonstrated success in modeling relational data primarily under the assumption of homophily. However, many real-world graphs exhibit heterophily,…
cs.LG2025
Exploring the Potential of Large Language Models for Heterophilic Graphs
Yuxia Wu, Shujie Li, Yuan Fang +1
Large language models (LLMs) have presented significant opportunities to enhance various machine learning applications, including graph neural networks (GNNs). By leveraging the va…