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• 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
• Large Language-Recommendation Assistant
• EXGC: Bridging Efficiency and Explainability in Graph Condensation
• 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
• 3D-MoLM: Towards 3D Molecule-Text Interpretation in Language Models
• Be Aware of the Neighborhood Effect: Modeling Selection Bias under Interference for Recommendation
• Boosting Few-shot Learning via Attentive Feature Regularization
• Text-to-Image Generation for Abstract Concepts
• Evaluating Post-hoc Explanations for Graph Neural Networks via Robustness Analysis
• Fairly Recommending with Social Attributes: A Flexible and Controllable Optimization Approach
• Generate What You Prefer: Reshaping Sequential Recommendation via Guided Diffusion
• Removing Hidden Confounding in Recommendation: A Unified Multi-Task Learning Approach
• Understanding Contrastive Learning via Distributionally Robust Optimization
• Unleashing the Power of Graph Data Augmentation on Covariate Shift
• 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
• CgT-GAN: CLIP-guided Text GAN for Image Captioning
• Semantic-based Selection, Synthesis, and Supervision for Few-shot Learning
• CIRS: Bursting Filter Bubbles by Counterfactual Interactive Recommender System
• On the Effectiveness of Sampled Softmax Loss for Item Recommendation
• RecAD: Towards A Unified Library for Recommender Attack and Defense
• Discriminative-Invariant Representation Learning for Unbiased Recommendation
• A Generic Learning Framework for Sequential Recommendation with Distribution Shifts
• Alleviating Matthew Effect of Offline Reinforcement Learning in Recommendation
• Diffusion Recommender Model
• Reformulating CTR Prediction: Learning Invariant Feature Interactions for Recommendation
• Learning to Double-check Model Prediction from a Causal Perspective
• Bi-directional Distribution Alignment for Transductive Zero Shot Learning
• Counterfactual Active Learning for Out-of-Distribution Generalization
• Alleviating Structural Distribution Shift in Graph Anomaly Detection
• Cooperative Explanations of Graph Neural Networks
• Unbiased Knowledge Distillation for Recommendation
• Enhance Image Classification via Inter-Class Image Mixup with Diffusion Model
• LightMIRM: Light Meta-learned Invariant Risk Minimization for Trustworthy Loan Default Prediction
• Adap-τ: Adaptively Modulating Embedding Magnitude for Recommendation
• Addressing Heterophily in Graph Anomaly Detection: A Perspective of Graph Spectrum
• GIF: A General Graph Unlearning Strategy via Influence Function
• On the Theories Behind Hard Negative Sampling for Recommendation
• Knowledge Graph Embedding by Normalizing Flows
• Inductive Lottery Ticket Learning for Graph Neural Networks
• Mitigating Hidden Confounding Effects for Causal Recommendation
• Popularity Bias Is Not Always Evil: Disentangling Benign and Harmful Bias for Recommendation
• KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos
• KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems
• Hierarchical Hourglass Convolutional Network for Efficient Video Classification
• Invariant Representation Learning for Multimedia Recommendation
• Multi-directional Knowledge Transfer for Few-Shot Learning
• Parameterization of Cross-Token Relations with Relative Positional Encoding for Vision MLP
• Unsupervised Video Hashing with Multi-granularity Contextualization and Multi-structure Preservation
• Exploring Lottery Ticket Hypothesis in Media Recommender Systems
• Addressing Unmeasured Confounder for Recommendation with Sensitivity Analysis
• Causal Attention for Interpretable and Generalizable Graph Classification
• Let Invariant Rationale Discovery Inspire Graph Contrastive Learning
• Reinforced Causal Explainer for Graph Neural Networks
• Interpolative Distillation for Unifying Biased and Debiased Recommendation
• Group Contextualization for Video Recognition
• Learning to Imagine: Integrating Counterfactual Thinking in Neural Discrete Reasoning
• A Survey on Neural Recommendation: From Collaborative Filtering to Content and Context Enriched Recommendation
• Addressing Confounding Feature Issue for Causal Recommendation
• Attention in Attention: Modeling Context Correlation for Efficient Video Classification
• 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
• GDSRec: Graph-Based Decentralized Collaborative Filtering for Social Recommendation
• Graph Convolution Machine for Context-aware Recommender System
• Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions
• Rumor Detection with Self-supervised Learning on Texts and Social Graph
• Time-aware Path Reasoning on Knowledge Graph for Recommendation
• Discovering Invariant Rationales for Graph Neural Networks
• Causal Representation Learning for Out-of-Distribution Recommendation
• Cross Pairwise Ranking for Unbiased Item Recommendation
• Interactive Hypergraph Neural Network for Personalized Product Search
• Learning Robust Recommenders through Cross-Model Agreement
• Towards Multi-Grained Explainability for Graph Neural Networks
• A Deep Learning Framework for Self-evolving Hierarchical Community Detection
• DisenKGAT: Knowledge Graph Embedding with Disentangled Graph Attention Network
• Selective Dependency Aggregation for Action Classification
• Deconfounded Recommendation for Alleviating Bias Amplification
• Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System
• AutoDebias: Learning to Debias for Recommendation
• 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
• Adversarial Attack on Large Scale Graph
• Cross-GCN: Enhancing Graph Convolutional Network with k-Order Feature Interactions
• Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-Start Users
• Disentangling User Interest and Conformity for Recommendation with Causal Embedding
• Learning Intents behind Interactions with Knowledge Graph for Recommendation
• On the Equivalence of Decoupled Graph Convolution Network and Label Propagation
• Advances and Challenges in Conversational Recommender Systems: A Survey
• Structure-Enhanced Meta-Learning For Few-Shot Graph Classification
• Interactive Path Reasoning on Graph for Conversational Recommendation
• Bundle Recommendation with Graph Convolutional Networks
• How to Retrain Recommender System? A Sequential Meta-Learning Approach
• LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
• Parameter-Efficient Transfer from Sequential Behaviors for User Modeling and Recommendation
• Bilinear Graph Neural Network with Neighbor Interactions
• Improving the Robustness of Wasserstein Embedding by Adversarial PAC-Bayesian Learning
• Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure
• Future Data Helps Training: Modelling Future Contexts for Session-based Recommendation
• Reinforced Negative Sampling over Knowledge Graph for Recommendation
• Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems
• Deep Learning for Matching in Search and Recommendation
• KGAT: Knowledge Graph Attention Network for Recommendation
• Modeling Extreme Events in Time Series Prediction
• λOpt: Learn to Regularize Recommender Models in Finer Levels
• Neural Graph Collaborative Filtering
• Relational Collaborative Filtering: Modeling Multiple Item Relations for Recommendation
• Semi-supervised User Profiling with Heterogeneous Graph Attention Networks
• Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preference
• Explainable Reasoning over Knowledge Graph Paths for Recommendation
• A Simple Convolutional Generative Network for Next-item 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
• Sequicity: Simplifying Task-oriented Dialogue Systems with Single Sequence-to-Sequence Architectures
• Adversarial Personalized Ranking for Recommendation
• Aesthetic-based Clothing Recommendation
• An Improved Sampler for Bayesian Personalized Ranking by Leveraging View Data
• 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
• BiRank: Towards Ranking on Bipartite Graphs
• Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks
• A Generic Coordinate Descent Framework for Learning from Implicit Feedback
• Neural Collaborative Filtering
• Context-aware Image Tweets Modelling and Recommendation
• Discrete Collaborative Filtering
• Fast Matrix Factorization for Online Recommendation with Implicit Feedback
• TriRank: Review-aware Explainable Recommendation by Modeling Aspects
• Relating an Image Tweet’s Text and Images
• Predicting the Popularity of Web 2.0 Items Based on User Comments
• Comment-based Multi-View Clustering of Web 2.0 Items