Latest

• Diffusion Models for Generative Outfit Recommendation
• Fair Recommendations with Limited Sensitive Attributes: A Distributionally Robust Optimization Approach
• Large Language Models are Learnable Planners for Long-Term Recommendation
• Leave No Patient Behind: Enhancing Medication Recommendation for Rare Disease Patients
• Improving Prostate Cancer Risk Prediction through Partial AUC Optimization
• Item-side Fairness of Large Language Model-based Recommendation System
• Lower-Left Partial AUC: An Effective and Efficient Optimization Metric for Recommendation
• Proactive Recommendation with Iterative Preference Guidance
• Be Aware of the Neighborhood Effect: Modeling Selection Bias under Interference for Recommendation
• LabelCraft: Empowering Short Video Recommendations with Automated Label Crafting
• Fairly Recommending with Social Attributes: A Flexible and Controllable Optimization Approach
• Removing Hidden Confounding in Recommendation: A Unified Multi-Task Learning Approach
• Leveraging Watch-time Feedback for Short-Video Recommendations: A Causal Labeling Framework
• Can ChatGPT Make Fair Recommendation? A Fairness Evaluation Benchmark for Recommendation with Large Language Model
• TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation
• RecAD: Towards A Unified Library for Recommender Attack and Defense
• Discriminative-Invariant Representation Learning for Unbiased Recommendation
• Diffusion Recommender Model
• Prediction then Correction: An Abductive Prediction Correction Method for Sequential Recommendation
• Reformulating CTR Prediction: Learning Invariant Feature Interactions for Recommendation
• Learning to Double-check Model Prediction from a Causal Perspective
• Understanding and Counteracting Feature-Level Bias in Click-Through Rate Prediction
• Counterfactual Active Learning for Out-of-Distribution Generalization
• Unbiased Knowledge Distillation for Recommendation
• LightMIRM: Light Meta-learned Invariant Risk Minimization for Trustworthy Loan Default Prediction
• On the Theories Behind Hard Negative Sampling for Recommendation
• Mitigating Hidden Confounding Effects for Causal Recommendation
• Invariant Representation Learning for Multimedia Recommendation
• Addressing Unmeasured Confounder for Recommendation with Sensitivity Analysis
• Reinforced Causal Explainer for Graph Neural Networks
• Interpolative Distillation for Unifying Biased and Debiased Recommendation
• Learning to Imagine: Integrating Counterfactual Thinking in Neural Discrete Reasoning
• Addressing Confounding Feature Issue for Causal Recommendation
• Bias and Debias in Recommender System: A Survey and Future Directions
• CatGCN: Graph Convolutional Networks with Categorical Node Features
• Causal Incremental Graph Convolution for Recommender System Retraining
• Causal Inference in Recommender Systems: A Survey and Future Directions
• Causal Representation Learning for Out-of-Distribution Recommendation
• Learning Robust Recommenders through Cross-Model Agreement
• Deconfounded Recommendation for Alleviating Bias Amplification
• Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System
• Causal Intervention for Leveraging Popularity Bias in Recommendation
• Clicks can be Cheating: Counterfactual Recommendation for Mitigating Clickbait Issue
• Self-supervised Graph Learning for Recommendation
• 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
• On the Equivalence of Decoupled Graph Convolution Network and Label Propagation
• Structure-Enhanced Meta-Learning For Few-Shot Graph Classification
• How to Retrain Recommender System? A Sequential Meta-Learning Approach
• Bilinear Graph Neural Network with Neighbor Interactions
• Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure
• Neural Graph Collaborative Filtering
• An Improved Sampler for Bayesian Personalized Ranking by Leveraging View Data
• Learning on Partial-Order Hypergraphs
• TEM: Tree-enhanced Embedding Model for Explainable Recommendation