25 citations · 50 across the 10 of their papers we have counts for
10 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…
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…
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…
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…
Unlocking the Hidden Treasures: Enhancing Recommendations with Unlabeled Data
Yuhan Zhao, Rui Chen, Qilong Han +2
Collaborative filtering (CF) stands as a cornerstone in recommender systems, yet effectively leveraging the massive unlabeled data presents a significant challenge. Current researc…
From Pairwise to Ranking: Climbing the Ladder to Ideal Collaborative Filtering with Pseudo-Ranking
Yuhan Zhao, Rui Chen, Li Chen +3
Intuitively, an ideal collaborative filtering (CF) model should learn from users' full rankings over all items to make optimal top-K recommendations. Due to the absence of such ful…
HACD: Harnessing Attribute Semantics and Mesoscopic Structure for Community Detection
Anran Zhang, Xingfen Wang, Yuhan Zhao
Community detection plays a pivotal role in uncovering closely connected subgraphs, aiding various real-world applications such as recommendation systems and anomaly detection. Wit…
Causality-Inspired Fair Representation Learning for Multimodal Recommendation
Weixin Chen, Li Chen, Yongxin Ni +1
Recently, multimodal recommendations (MMR) have gained increasing attention for alleviating the data sparsity problem of traditional recommender systems by incorporating modality-b…
Augmented Negative Sampling for Collaborative Filtering
Yuhan Zhao, Rui Chen, Riwei Lai +3
Negative sampling is essential for implicit-feedback-based collaborative filtering, which is used to constitute negative signals from massive unlabeled data to guide supervised lea…