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论文Awesome Recsys
I share information related to the Recommender Systems that I am interested in. They consist of SIGIR, RecSys, ICLR, NeurIPS, ICML, AAAI, IJCAI, KDD, etc.
- modified: 2024-01-13
Conference Paper
SIGIR, Recsys, WSDM, KDD, etc.
None means unavailable URL or papers that have not been published yet.
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2023
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2022
2023
WSDM 2023
- Search Behavior Prediction: A Hypergraph Perspective
- CL4CTR: A Contrastive Learning Framework for CTR Prediction
- IDNP: Interest Dynamics Modeling using Generative Neural Processes for Sequential Recommendation
- Learning to Distinguish Multi-User Coupling Behaviors for TV Recommendation
- Towards Universal Cross-Domain Recommendation
- One for All, All for One: Learning and Transferring User Embeddings for Cross-Domain Recommendation
- Slate-Aware Ranking for Recommendation
- Directed Acyclic Graph Factorization Machines for CTR Prediction via Knowledge Distillation
- Knowledge Enhancement for Contrastive Multi-Behavior Recommendation
- Disentangled Representation for Diversified Recommendations
- Heterogeneous Graph-based Context-aware Document Ranking
- Cognition-aware Knowledge Graph Reasoning for Explainable Recommendation
- Separating Examination and Trust Bias from Click Predictions for Unbiased Relevance Ranking
- Self-Supervised Group Graph Collaborative Filtering for Group Recommendation
- Learning Topical Stance Embeddings from Signed Social Graphs
- Calibrated Recommendations as a Minimum-Cost Flow Problem
- Search Behavior Prediction: A Hypergraph Perspective
- DisenPOI: Disentangling Sequential and Geographical Influence for Point-of-Interest Recommendation
- Multi-Intentions Oriented Contrastive Learning for Sequential Recommendation
- MUSENET: Multi-Scenario Learning for Repeat-Aware Personalized Recommendation
- A Personalized Neighborhood-based Model for Within-basket Recommendation in Grocery Shopping
- Disentangled Negative Sampling for Collaborative Filtering
- DIGMN: Dynamic Intent Guided Meta Network for Differentiated User Engagement Forecasting in Online Professional Social Platforms
- SGCCL: Siamese Graph Contrastive Consensus Learning for Personalized Recommendation
- Multimodal Pre-Training with Self-Distillation for Product Understanding in E-Commerce
- Relation Preference oriented High-order Sampling for Recommendation
- Variational Reasoning over Incomplete Knowledge Graphs for Conversational Recommendation
- Exploiting Explicit and Implicit Item relationships for Session-based Recommendation
- Meta Policy Learning for Cold-Start Conversational Recommendation
- Efficiently Leveraging Multi-level User Intent for Session-based Recommendation via Atten-Mixer Network
- Simplifying Graph-based Collaborative Filtering for Recommendation
- AutoGen: An Automated Dynamic Model Generation Framework for Recommender System
- A Causal View for Item-level Effect of Recommendation on User Preference
- Federated Unlearning for On-Device Recommendation
- Counterfactual Collaborative Reasoning
- Uncertainty Quantification for Fairness in Two-Stage Recommender Systems
- Generating Explainable Product Comparisons for Online Shopping
- Unbiased Knowledge Distillation for Recommendation
- VRKG4Rec: Virtual Relational Knowledge Graph for Recommendation
- Knowledge-Adaptive Contrastive Learning for Recommendation
- Heterogeneous Graph Contrastive Learning for Recommendation
ICLR 2023
- Calibration Matters: Tackling Maximization Bias in Large-scale Advertising Recommendation Systems
- ResAct: Reinforcing Long-term Engagement in Sequential Recommendation with Residual Actor
- Personalized Reward Learning with Interaction-Grounded Learning (IGL)
- TDR-CL: Targeted Doubly Robust Collaborative Learning for Debiased Recommendations
- Online Low Rank Matrix Completion
- StableDR: Stabilized Doubly Robust Learning for Recommendation on Data Missing Not at Random
- MaskFusion: Feature Augmentation for Click-Through Rate Prediction via Input-adaptive Mask Fusion
- LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation
AAAI 2023
WWW 2023
- Multi-Modal Self-Supervised Learning for Recommendation
- Collaboration-Aware Graph Convolutional Network for Recommender Systems
- Enhancing Hierarchy-Aware Graph Networks with Deep Dual Clustering for Session-based Recommendation
- ConsRec: Learning Consensus Behind Interactions for Group Recommendation
- Semi-decentralized Federated Ego Graph Learning for Recommendation
- Joint Internal Multi-Interest Exploration and External Domain Alignment for Cross Domain Sequential Recommendation
- Intra and Inter Domain HyperGraph Convolutional Network for Cross-Domain Recommendation
- Dual Intent Enhanced Graph Neural Network for Session-based New Item Recommendation
- ApeGNN: Node-Wise Adaptive Aggregation in GNNs for Recommendation
- Enhancing User Personalization in Conversational Recommenders
- LINet: A Location and Intention-Aware Neural Network for Hotel Group Recommendation
- Multi-Modal Self-Supervised Learning for Recommendation
- Distillation from Heterogeneous Models for Top-K Recommendation
- On the Theories Behind Hard Negative Sampling for Recommendation
- Fine-tuning Partition-aware Item Similarities for Efficient and Scalable Recommendation
- Exploration and Regularization of the Latent Action Space in Recommendation
- Bootstrap Latent Representations for Multi-modal Recommendation
- Two-Stage Constrained Actor-Critic for Short Video Recommendation
- Recommendation with Causality enhanced Natural Language Explanations
- Cross-domain recommendation via user interest alignment
- Robust Recommendation with Adversarial Gaussian Data Augmentation
- Dual-interest Factorization-heads Attention for Sequential Recommendation
- Contrastive Collaborative Filtering for Cold-Start Item Recommendation
- Anti-FakeU: Defending Shilling Attacks on Graph Neural Network based Recommender Model
- Compressed Interaction Graph based Framework for Multi-behavior Recommendation
- A Counterfactual Collaborative Session-based Recommender System
- Correlative Preference Transfer with Hierarchical Hypergraph Network for Multi-Domain Recommendation
- Automated Self-Supervised Learning for Recommendation
- AutoDenoise: Automatic Data Instance Denoising for Recommendations
- Improving Recommendation Fairness via Data Augmentation
- ColdNAS: Search to Modulate for User Cold-Start Recommendation: ColdNAS
- AutoS2AE: Automate to Regularize Sparse Shallow Autoencoders for Recommendation
- Quantize Sequential Recommenders Without Private Data
- Interaction-level Membership Inference Attack Against Federated Recommender Systems
- Debiased Contrastive Learning for Sequential Recommendation
- Clustered Embedding Learning for Recommender Systems
- Adap-τ: Adaptively Modulating Embedding Magnitude for Recommendation
- Robust Preference-Guided Denoising for Graph based Social Recommendation
- MMMLP: Multi-modal Multilayer Perceptron for Sequential Recommendations
- Few-shot News Recommendation via Cross-lingual Transfer
- User Retention-oriented Recommendation with Decision Transformer
- Cooperative Retriever and Ranker in Deep Recommenders
- Learning Vector-Quantized Item Representation for Transferable Sequential Recommenders
- Show Me The Best Outfit for A Certain Scene: A Scene-aware Fashion Recommender System
- Multi-Behavior Recommendation with Cascading Graph Convolutional Network
- AutoMLP: Automated MLP for Sequential Recommendations
- NASRec: Weight Sharing Neural Architecture Search for Recommender Systems
- Membership Inference Attacks Against Sequential Recommender Systems
- Communicative MARL-based Relevance Discerning Network for Repetition-Aware Recommendation
- Modeling Temporal Positive and Negative Excitation for Sequential Recommendation
- Multi-Task Recommendations with Reinforcement Learning
- A Self-Correcting Sequential Recommender
- [Cross-domain Recommendation
