404 citations · 590 across the 12 of their papers we have counts for
16 papers
Knowledge Graph Completion with Counterfactual Augmentation
Heng Chang, Jie Cai, Jia Li
Graph Neural Networks (GNNs) have demonstrated great success in Knowledge Graph Completion (KGC) by modeling how entities and relations interact in recent years. However, most of t…
Generating Negative Samples for Sequential Recommendation
Yongjun Chen, Jia Li, Zhiwei Liu +4
To make Sequential Recommendation (SR) successful, recent works focus on designing effective sequential encoders, fusing side information, and mining extra positive self-supervisio…
MACE: An Efficient Model-Agnostic Framework for Counterfactual Explanation
Wenzhuo Yang, Jia Li, Caiming Xiong +1
Counterfactual explanation is an important Explainable AI technique to explain machine learning predictions. Despite being studied actively, existing optimization-based methods oft…
Rethinking Graph Neural Networks for Anomaly Detection
Jianheng Tang, Jiajin Li, Ziqi Gao +1
Graph Neural Networks (GNNs) are widely applied for graph anomaly detection. As one of the key components for GNN design is to select a tailored spectral filter, we take the first…
ELECRec: Training Sequential Recommenders as Discriminators
Yongjun Chen, Jia Li, Caiming Xiong
Sequential recommendation is often considered as a generative task, i.e., training a sequential encoder to generate the next item of a user's interests based on her historical inte…
Improving Contrastive Learning with Model Augmentation
Zhiwei Liu, Yongjun Chen, Jia Li +3
The sequential recommendation aims at predicting the next items in user behaviors, which can be solved by characterizing item relationships in sequences. Due to the data sparsity a…