9 papers
Give the Long-tail More SPACE: Promoting Provider Fairness in Next POI Recommendation
Anran Zhang, Jiaqi Jiang, jiahui Jin +1
Next point-of-interest (POI) recommendation predicts users' future destinations from historical mobility sequences and has become a key component of location-based services. Howeve…
HiSE: A Lightweight Hierarchical Semantic Explainer for Heterogeneous Graph Neural Networks
Zongrui Li, Yuhang Zhao, Ying Zhao +3
Heterogeneous graph neural networks (HGNNs) have demonstrated remarkable performance in modeling complex relational data, however their interpretability in high-stakes applications…
MemRec: Collaborative Memory-Augmented Agentic Recommender System
Weixin Chen, Yuhan Zhao, Jingyuan Huang +6
The evolution of recommender systems has shifted from traditional collaborative filtering to LLM-based agentic systems, which rely on semantic user and item memories to make predic…
The Double-Edged Sword of Knowledge Transfer: Diagnosing and Curing Fairness Pathologies in Cross-Domain Recommendation
Yuhan Zhao, Weixin Chen, Li Chen +1
Cross-domain recommendation (CDR) offers an effective strategy for improving recommendation quality in a target domain by leveraging auxiliary signals from source domains. Nonethel…
Post-Training Fairness Control: A Single-Train Framework for Dynamic Fairness in Recommendation
Weixin Chen, Li Chen, Yuhan Zhao
Despite growing efforts to mitigate unfairness in recommender systems, existing fairness-aware methods typically fix the fairness requirement at training time and provide limited p…
Leave No One Behind: Fairness-Aware Cross-Domain Recommender Systems for Non-Overlapping Users
Weixin Chen, Yuhan Zhao, Li Chen +1
Cross-domain recommendation (CDR) methods predominantly leverage overlapping users to transfer knowledge from a source domain to a target domain. However, through empirical studies…