tat-sengchua

Latest

• Diffusion Recommender Model
• Learning to Double-check Model Prediction from a Causal Perspective
• Cooperative Explanations of Graph Neural Networks
• Inductive Lottery Ticket Learning for Graph Neural Networks
• KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems
• Causal Attention for Interpretable and Generalizable Graph Classification
• Let Invariant Rationale Discovery Inspire Graph Contrastive Learning
• Reinforced Causal Explainer for Graph Neural Networks
• Learning to Imagine: Integrating Counterfactual Thinking in Neural Discrete Reasoning
• Discovering Invariant Rationales for Graph Neural Networks
• Causal Representation Learning for Out-of-Distribution Recommendation
• Towards Multi-Grained Explainability for Graph Neural Networks
• Deconfounded Recommendation for Alleviating Bias Amplification
• Clicks can be Cheating: Counterfactual Recommendation for Mitigating Clickbait Issue
• Should Graph Convolution Trust Neighbors? A Simple Causal Inference Method
• Denoising Implicit Feedback for Recommendation
• Cross-GCN: Enhancing Graph Convolutional Network with k-Order Feature Interactions
• Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-Start Users
• Learning Intents behind Interactions with Knowledge Graph for Recommendation
• Advances and Challenges in Conversational Recommender Systems: A Survey
• Interactive Path Reasoning on Graph for Conversational Recommendation
• Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure
• Reinforced Negative Sampling over Knowledge Graph for Recommendation
• Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems
• KGAT: Knowledge Graph Attention Network for Recommendation
• Neural Graph Collaborative Filtering
• Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preference
• Explainable Reasoning over Knowledge Graph Paths for Recommendation
• Fast Matrix Factorization with Non-Uniform Weights on Missing Data
• Knowledge-aware Multimodal Dialog Systems
• Attributed Social Network Embedding
• NAIS: Neural Attentive Item Similarity Model for Recommendation
• Adversarial Personalized Ranking for Recommendation
• Learning on Partial-Order Hypergraphs
• TEM: Tree-enhanced Embedding Model for Explainable Recommendation
• Attentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-level Attention
• Item Silk Road: Recommending Items from Information Domains to Social Users
• Neural Factorization Machines for Sparse Predictive Analytics
• Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks
• Neural Collaborative Filtering
• Fast Matrix Factorization for Online Recommendation with Implicit Feedback