77 citations · 429 across the 30 of their papers we have counts for
11 papers · 1 filter
Nothing Stands Alone: Relational Fake News Detection with Hypergraph Neural Networks
Ujun Jeong, Kaize Ding, Lu Cheng +3
Nowadays, fake news easily propagates through online social networks and becomes a grand threat to individuals and society. Assessing the authenticity of news is challenging due to…
Distributional Shift Adaptation using Domain-Specific Features
Anique Tahir, Lu Cheng, Ruocheng Guo +1
Machine learning algorithms typically assume that the training and test samples come from the same distributions, i.e., in-distribution. However, in open-world scenarios, streaming…
CLEAR: Generative Counterfactual Explanations on Graphs
Jing Ma, Ruocheng Guo, Saumitra Mishra +2
Counterfactual explanations promote explainability in machine learning models by answering the question "how should an input instance be perturbed to obtain a desired predicted lab…
Mitigating Popularity Bias in Recommendation with Unbalanced Interactions: A Gradient Perspective
Weijieying Ren, Lei Wang, Kunpeng Liu +3
Recommender systems learn from historical user-item interactions to identify preferred items for target users. These observed interactions are usually unbalanced following a long-t…
MLP4Rec: A Pure MLP Architecture for Sequential Recommendations
Muyang Li, Xiangyu Zhao, Chuan Lyu +3
Self-attention models have achieved state-of-the-art performance in sequential recommender systems by capturing the sequential dependencies among user-item interactions. However, t…
Causal Disentanglement with Network Information for Debiased Recommendations
Paras Sheth, Ruocheng Guo, Lu Cheng +2
Recommender systems aim to recommend new items to users by learning user and item representations. In practice, these representations are highly entangled as they consist of inform…