activity
20222025
most citedJoint Local Relational Augmentation and Global Nash Equilibrium for Federated Learning with Non-IID Data

10 citations · 23 across the 15 of their papers we have counts for

collaborators
Showing cs.IRShow all

10 papers · 1 filter

cs.IR2025

Distilling Transitional Pattern to Large Language Models for Multimodal Session-based Recommendation

Jiajie Su, Qiyong Zhong, Yunshan Ma +5

Session-based recommendation (SBR) predicts the next item based on anonymous sessions. Traditional SBR explores user intents based on ID collaborations or auxiliary content. To fur…

cs.IR20251 cited

Joint Similarity Item Exploration and Overlapped User Guidance for Multi-Modal Cross-Domain Recommendation

Weiming Liu, Chaochao Chen, Jiahe Xu +6

Cross-Domain Recommendation (CDR) has been widely investigated for solving long-standing data sparsity problem via knowledge sharing across domains. In this paper, we focus on the…

cs.IR20241 cited

Personalized Behavior-Aware Transformer for Multi-Behavior Sequential Recommendation

Jiajie Su, Chaochao Chen, Zibin Lin +3

Sequential Recommendation (SR) captures users' dynamic preferences by modeling how users transit among items. However, SR models that utilize only single type of behavior interacti…

cs.IR20235 cited

In-processing User Constrained Dominant Sets for User-Oriented Fairness in Recommender Systems

Zhongxuan Han, Chaochao Chen, Xiaolin Zheng +4

Recommender systems are typically biased toward a small group of users, leading to severe unfairness in recommendation performance, i.e., User-Oriented Fairness (UOF) issue. The ex…

cs.IR20235 cited

PPGenCDR: A Stable and Robust Framework for Privacy-Preserving Cross-Domain Recommendation

Xinting Liao, Weiming Liu, Xiaolin Zheng +2

Privacy-preserving cross-domain recommendation (PPCDR) refers to preserving the privacy of users when transferring the knowledge from source domain to target domain for better perf…

cs.IR20223 cited

DDGHM: Dual Dynamic Graph with Hybrid Metric Training for Cross-Domain Sequential Recommendation

Xiaolin Zheng, Jiajie Su, Weiming Liu +1

Sequential Recommendation (SR) characterizes evolving patterns of user behaviors by modeling how users transit among items. However, the short interaction sequences limit the perfo…