| Bipartite Graph Embedding via Mutual Information Maximization |
| Unbiased Learning to Rank in Feeds Recommendation |
| DeepLight: Deep Lightweight Feature Interactions for Accelerating CTR Predictions in Ad Serving |
| Pre-Training Graph Neural Networks for Cold-Start Users and Items Representation |
| Sparse-Interest Network for Sequential Recommendation |
| Alleviating Cold-Start Problems in Recommendation through Pseudo-Labelling over Knowledge Graph |
| Adapting User Preference to Online Feedback in Multi-round Conversational Recommendation |
| Beyond Point Estimate: Inferring Ensemble Prediction Variation from Neuron Activation Strength in Recommender Systems |
| Combating Selection Biases in Recommender Systems with A Few Unbiased Ratings |
| Denoising Implicit Feedback for Recommendation |
| Decomposed Collaborative Filtering: Modeling Explicit and Implicit Factors For Recommender Systems |
| Learning User Representations with Hypercuboids for Recommender Systems |
| Real-time Relevant Recommendation Suggestion |
| Explainable Recommendation with Comparative Constraints on Product Aspects |
| Bilateral Variational Autoencoder for Collaborative Filtering |
| Origin-Aware Next Destination Recommendation with Personalized Preference Attention |
| Practical Compositional Fairness: Understanding Fairness in Multi-Component Recommender Systems |
| Multi-Interactive Attention Network for Fine-grained Feature Learning in CTR Prediction |
| Enhancing Neural Recommender Models through Domain-Specific Concordance |
| An Efficient and Effective Framework for Session-based Social Recommendation |
| Popularity-Opportunity Bias in Collaborative Filtering |
| Towards Long-term Fairness in Recommendation |
| Causal Transfer Random Forest: Combining Logged Data and Randomized Experiments for Robust Prediction |
| Explanation as a Defense of Recommendation |
| Heterogeneous Graph Augmented Multi-Scenario Sharing Recommendation with Tree-Guided Expert Networks |
| A Black-Box Attack Model for Visually-Aware Recommenders |
| User Response Models to Improve a REINFORCE Recommender System |