3 citations · 5 across the 5 of their papers we have counts for
5 papers
Federated Learning and Unlearning for Recommendation with Personalized Data Sharing
Liang Qu, Jianxin Li, Wei Yuan +4
Federated recommender systems (FedRS) have emerged as a paradigm for protecting user privacy by keeping interaction data on local devices while coordinating model training through…
CADRL: Category-aware Dual-agent Reinforcement Learning for Explainable Recommendations over Knowledge Graphs
Shangfei Zheng, Hongzhi Yin, Tong Chen +3
Knowledge graphs (KGs) have been widely adopted to mitigate data sparsity and address cold-start issues in recommender systems. While existing KGs-based recommendation methods can…
Do as I can, not as I get
Shangfei Zheng, Hongzhi Yin, Tong Chen +3
This paper proposes a model called TMR to mine valuable information from simulated data environments. We intend to complete the submission of this paper.
DREAM: Adaptive Reinforcement Learning based on Attention Mechanism for Temporal Knowledge Graph Reasoning
Shangfei Zheng, Hongzhi Yin, Tong Chen +3
Temporal knowledge graphs (TKGs) model the temporal evolution of events and have recently attracted increasing attention. Since TKGs are intrinsically incomplete, it is necessary t…
MMKGR: Multi-hop Multi-modal Knowledge Graph Reasoning
Shangfei Zheng, Weiqing Wang, Jianfeng Qu +3
Multi-modal knowledge graphs (MKGs) include not only the relation triplets, but also related multi-modal auxiliary data (i.e., texts and images), which enhance the diversity of kno…