4 papers
ReliGRec: Reliability-Oriented LLM-Based Generative Recommendation via User-Risk-Aware Prompt Routing
Haoran Yang, Fei Chen, Yutian Xiao +1
User behavior in real-world recommender systems is heterogeneous. While some users exhibit coherent preferences, others show abrupt interest shifts, bursty interactions, excessive…
Learn Weightlessness: Imitate Non-Self-Stabilizing Motions on Humanoid Robot
Yucheng Xin, Jiacheng Bao, Haoran Yang +6
The integration of imitation and reinforcement learning has enabled remarkable advances in humanoid whole-body control, facilitating diverse human-like behaviors. However, research…
Large Language Model Enhanced Graph Invariant Contrastive Learning for Out-of-Distribution Recommendation
Jiahao Liang, Haoran Yang, Xiangyu Zhao +4
Out-of-distribution (OOD) generalization has emerged as a significant challenge in graph recommender systems. Traditional graph neural network algorithms often fail because they le…
Democratic Recommendation with User and Item Representatives Produced by Graph Condensation
Jiahao Liang, Haoran Yang, Xiangyu Zhao +4
The challenges associated with large-scale user-item interaction graphs have attracted increasing attention in graph-based recommendation systems, primarily due to computational in…