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 : 2023-06-01
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
- 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
- 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
- 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 with Behavioral Importance Perception
- Balancing Unobserved Confounding with a Few Unbiased Ratings in Debiased Recommendations
- Code Recommendation for Open Source Software Developers
- Denoising and Prompt-Tuning for Multi-Behavior Recommendation
- Mutual Wasserstein Discrepancy Minimization for Sequential Recommendation
- Confident Action Decision via Hierarchical Policy Learning for Conversational Recommendation
- CAMUS: Attribute-Aware Counterfactual Augmentation for Minority Users in Recommendation
- Dynamically Expandable Graph Convolution for Streaming Recommendation
- Dual Policy Learning for Aggregation Optimization in Graph Neural Network-based Recommender Systems
- Automatic Feature Selection By One-Shot Neural Architecture Search In Recommendation Systems
- Semi-supervised Adversarial Learning for Complementary Item Recommendation
- RL-MPCA: A Reinforcement Learning Based Multi-Phase Computation Allocation Approach for Recommender Systems
- Learning with Exposure Constraints in Recommendation Systems
- Maximizing Submodular Functions for Recommendation in the Presence of Biases
- Scoping Fairness Objectives and Identifying Fairness Metrics for Recommender Systems: The Practitioners’ Perspective
- P-MMF: Provider Max-min Fairness Re-ranking in Recommender System
- Fairly Adaptive Negative Sampling for Recommendations
- Mitigating the Filter Bubble While Maintaining Relevance: Targeted Diversification with VAE-based Recommender Systems
- Decoupled Side Information Fusion for Sequential Recommendation
- Hierarchical Multi-Task Graph Recurrent Network for Next POI Recommendation
- Interpolative Distillation for Unifying Biased and Debiased Recommendation
- Locality-Sensitive State-Guided Experience Replay Optimization for Sparse-Reward in Online Recommendation
- Unify Local and Global Information for Top-N Recommendation
- Co-training Disentangled Domain Adaptation Network for Leveraging Popularity Bias in Recommenders
- User-Aware Multi-Interest Learning for Candidate Matching in Recommenders
- Post Processing Recommender Systems with Knowledge Graphs for Recency, Popularity, and Diversity of Explanations
- Multi-Level Interaction Reranking with User Behavior History
- User-controllable Recommendation Against Filter Bubbles
- DisenCDR: Learning Disentangled Representations for Cross-Domain Recommendation
- Thinking inside The Box: Learning Hypercube Representations for Group Recommendation
- A Review-aware Graph Contrastive Learning Framework for Recommendation
- Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation
- On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation
- Multi-Agent RL-based Information Selection Model for Sequential Recommendation
- Knowledge Graph Contrastive Learning for Recommendation
- Enhancing CTR Prediction with Context-Aware Feature Representation Learning
- Joint Multisided Exposure Fairness for Recommendation
- When Multi-Level Meets Multi-Interest: A Multi-Grained Neural Model for Sequential Recommendation
- Rethinking Reinforcement Learning for Recommendation: A Prompt Perspective
- Single-shot Embedding Dimension Search in Recommender System
- Learning to Infer User Implicit Preference in Conversational Recommendation
- Doubly-Adaptive Reinforcement Learning for Cross-Domain Interactive Recommendation
- An Attribute-Driven Mirroring Graph Network for Session-based Recommendation
- Geometric Disentangled Collaborative Filtering
- Hypergraph Contrastive Collaborative Filtering
- Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System
- User-Centric Conversational Recommendation with Multi-Aspect User Modeling
- MGPolicy: Meta Graph Enhanced Off-policy Learning for Recommendations
- HIEN: Hierarchical Intention Embedding Network for Click-Through Rate Prediction
- Webformer: Pre-training with Web Pages for Information Retrieval
- Forest-based Deep Recommender
- Bilateral Self-unbiased Recommender Learning from Biased Implicit Feedback
- Price DOES Matter! Modeling Price and Interest Preferences in Session-based Recommendation
- Privacy-Preserving Synthetic Data Generation for Recommendation
- Investigating Accuracy-Novelty Performance for Graph-based Collaborative Filtering
- DAWAR: Diversity-aware Web APIs Recommendation for Mashup Creation based on Correlation Graph
- Variational Reasoning about User Preferences for Conversational Recommendation
- Alleviating Spurious Correlations in Knowledge-aware Recommendations through Counterfactual Generator
- NAS-CTR: Efficient Neural Architecture Search for Click-Through Rate Prediction
- Exploiting Variational Domain-Invariant User Embedding for Partially Overlapped Cross Domain Recommendation
- Analyzing and Simulating User Utterance Reformulation in Conversational Recommender Systems
- HAKG: Hierarchy-Aware Knowledge Gated Network for Recommendation
- Self-Guided Learning to Denoise for Robust Recommendation
- AutoGSR: Neural Architecture Search for Graph-based Session Recommendation
- Learning Graph-based Disentangled Representations for Next POI Recommendation
- GETNext: Trajectory Flow Map Enhanced Transformer for Next POI Recommendation
- Ada-Ranker: A Data Distribution Adaptive Ranking Paradigm for Sequential Recommendation
- Learning to Denoise Unreliable Interactions for Graph Collaborative Filtering
- INMO: A Model-Agnostic and Scalable Module for Inductive Collaborative Filtering
- ProFairRec: Provider Fairness-aware News Recommendation
- Multi-Faceted Global Item Relation Learning for Session-Based Recommendation
- ReCANet: A Repeat Consumption-Aware Neural Network for Next Basket Recommendation in Grocery Shopping
- Neighbour Interaction based Click-Through Rate Prediction via Graph-masked Transformer
- CAPTOR: A Crowd-Aware Pre-Travel Recommender System for Out-of-Town Users
- Multi-Behavior Sequential Transformer Recommender
- Deployable and Continuable Meta-Learning-Based Recommender System with Fast User-Incremental Updates
- Explainable Fairness for Feature-aware Recommender Systems
- Graph Trend Filtering Networks for Recommendation
- AutoLossGen: Automatic Loss Function Generation for Recommender Systems
- Determinantal Point Process Set Likelihood-Based Loss Functions for Sequential Recommendation
- KETCH: Knowledge Graph Enhanced Thread Recommendation in Healthcare Forums
- CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systems
- PEVAE: A hierarchical VAE for personalized explainable recommendation.
- Positive, Negative and Neutral: Modeling Implicit Feedback in Session-based News Recommendation
- Less is More: Reweighting Important Spectral Graph Features for Recommendation
- A GPU-specialized Inference Parameter Server for Large-Scale Deep Recommendation Models
- A User-Centered Investigation of Personal Music Tours
- Adversary or Friend? An adversarial Approach to Improving Recommender Systems
- Aspect Re-distribution for Learning Better Item Embeddings in Sequential Recommendation
- BRUCE – Bundle Recommendation Using Contextualized item Embeddings
- Bundle MCR: Towards Conversational Bundle Recommendation
- CAEN: A Hierarchically Attentive Evolution Network for Item-Attribute-Change-Aware Recommendation in the Growing E-commerce Environment
- Context and Attribute-Aware Sequential Recommendation via Cross-Attention
- Countering Popularity Bias by Regularizing Score Differences
- Defending Substitution-based Profile Pollution Attacks on Sequential Recommenders
- Denoising Self-Attentive Sequential Recommendation
- Don’t recommend the obvious: estimate probability ratios
- Dual Attentional Higher Order Factorization Machines
- Dynamic Global Sensitivity for Differentially Private Contextual Bandits
- EANA: Reducing Privacy Risk on Large-scale Recommendation Models
- Effective and Efficient Training for Sequential Recommendation using Recency Sampling
- Exploiting Negative Preference in Content-based Music Recommendation with Contrastive Learning
- Exploring the longitudinal effects of nudging on users’ music genre exploration behavior and listening preferences
- Fairness-aware Federated Matrix Factorization
- Fast And Accurate User Cold-Start Learning Using Monte Carlo Tree Search
- Global and Personalized Graphs for Heterogeneous Sequential Recommendation by Learning Behavior Transitions and User Intentions
- Identifying New Podcasts with High General Appeal Using a Pure Exploration Infinitely-Armed Bandit Strategy
- Learning Recommendations from User Actions in the Item-poor Insurance Domain
- Learning to Ride a Buy-Cycle: A Hyper-Convolutional Model for Next Basket Repurchase Recommendation
- MARRS: A Framework for multi-objective risk-aware route recommendation using Multitask-Transformer
- Modeling Two-Way Selection Preference for Person-Job Fit
- Modeling User Repeat Consumption Behavior for Online Novel Recommendation
- Multi-Modal Dialog State Tracking for Interactive Fashion Recommendation
- Off-Policy Actor Critic for Recommender Systems
- ProtoMF: Prototype-based Matrix Factorization for Effective and Explainable Recommendations
- RADio – Rank-Aware Divergence Metrics to Measure Normative Diversity in News Recommendations
- Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)
- Reducing Cross-Topic Political Homogenization in Content-Based News Recommendation
- Self-Supervised Bot Play for Transcript-Free Conversational Recommendation with Rationales
- Solving Diversity-Aware Maximum Inner Product Search Efficiently and Effectively
- TinyKG: Memory-Efficient Training Framework for Knowledge Graph Neural Recommender Systems
- Toward Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneity
- Towards Psychologically Grounded Dynamic Preference Models
- You Say Factorization Machine, I Say Neural Network – It’s All in the Activation
- Revisiting the Performance of iALS on Item Recommendation Benchmarks
- Comprehensive Fair Meta-learned Recommender System
- Graph-Flashback Network for Next Location Recommendation
- Learning Binarized Graph Representations with Multi-faceted Quantization Reinforcement for Top-K Recommendation
- GUIDE: Group Equality Informed Individual Fairness in Graph Neural Networks
- Detecting Arbitrary Order Beneficial Feature Interactions for Recommender Systems
- Practical Counterfactual Policy Learning for Top-K Recommendations
- Addressing Unmeasured Confounder for Recommendation with Sensitivity Analysis
- Towards Representation Alignment and Uniformity in Collaborative Filtering
- Knowledge-enhanced Black-box Attacks for Recommendations
- Towards Universal Sequence Representation Learning for Recommender Systems
- Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learning
- Debiasing Learning for Membership Inference Attacks Against Recommender Systems
- Debiasing the Cloze Task in Sequential Recommendation with Bidirectional Transformers
- User-Event Graph Embedding Learning for Context-Aware Recommendation
- Aligning Dual Disentangled User Representations from Ratings and Textual Content
- Fair Ranking as Fair Division: Impact-Based Individual Fairness in Ranking
- Make Fairness More Fair: Fair Item Utility Estimation and Exposure Re-Distribution
- Invariant Preference Learning for General Debiasing in Recommendation
- PARSRec: Explainable Personalized Attention-fused Recurrent Sequential Recommendation Using Session Partial Actions
- CrossCBR: Cross-view Contrastive Learning for Bundle Recommendation
- HICF: Hyperbolic Informative Collaborative Filtering
- Extracting Relevant Information from User's Utterances in Conversational Search and Recommendation
- Counteracting User Attention Bias in Music Streaming Recommendation via Reward Modification
- Adversarial Gradient Driven Exploration for Deep Click-Through Rate Prediction
- Self-Supervised Hypergraph Transformer for Recommender Systems
- PinnerFormer: Sequence Modeling for User Representation at Pinterest
- TwHIN: Embedding the Twitter Heterogeneous Information Network for Personalized Recommendation
- A Biased Sampling Method for Imbalanced Personalized Ranking
- Accurate Action Recommendation for Smart Home via Two-Level Encoders and Commonsense Knowledge
- Adapting Triplet Importance of Implicit Feedback for Personalized Recommendation
- An Uncertainty-Aware Imputation Framework for Alleviating the Sparsity Problem in Collaborative Filtering
- Asymmetrical Context-aware Modulation for Collaborative Filtering Recommendation
- Automatic Meta-Path Discovery for Effective Graph-Based Recommendation
- Beyond Learning from Next Item: Sequential Recommendation via Personalized Interest Sustainabilit
- ContrastVAE: Contrastive Variational AutoEncoder for Sequential Recommendation
- Cross-Domain Aspect Extraction using Transformers Augmented with Knowledge Graphs
- Cross-domain Cross-architecture Black-box Attacks on Fine-tuned Models with Transferred Evolutionary Strategies
- Cross-domain Recommendation via Adversarial Adaptation
- Disentangling Past-Future Modeling in Sequential Recommendation via Dual Networks
- Domain-Agnostic Constrastive Representations for Learning from Label Proportions
- Dual-Task Learning for Multi-Behavior Sequential Recommendation
- Dynamic Causal Collaborative Filtering
- Dynamic Hypergraph Learning for Collaborative Filtering
- Graph Based Long-Term And Short-Term Interest Model for Click-Through Rate Prediction
- Gromov-Wasserstein Guided Representation Learning for Cross-Domain Recommendation
- Hierarchical Item Inconsistency Signal learning for Sequence Denoising in Sequential Recommendation
- Hierarchically Fusing Long and Short-Term User Interests for Click-Through Rate Prediction in Product Search
- HySAGE: A Hybrid Static and Adaptive Graph Embedding Network for Context-Drifting Recommendations
- ITSM-GCN: Informative Training Sample Mining for Graph Convolution Network-based Collaborative Filtering
- KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems
- MARIO: Modality-Aware Attention and Modality-Preserving Decoders for Multimedia Recommendation
- Memory Bank Augmented Long-tail Sequential Recommendation
- Multi-level Contrastive Learning Framework for Sequential Recommendation
- OptEmbed: Learning Optimal Embedding Table for Click-through Rate Prediction
- Quantifying and Mitigating Popularity Bias in Conversational Recommender Systems
- Review-Based Domain Disentanglement without Duplicate Users or Contexts for Cross-Domain Recommendation
- SVD-GCN: A Simplified Graph Convolution Paradigm for Recommendation
- Spatiotemporal-aware Session-based Recommendation with Graph Neural Networks
- MAE4Rec: Storage-saving Transformer for Sequential Recommendations
- Target Interest Distillation for Multi-Interest Recommendation
- The Interaction Graph Auto-encoder Network Based on Topology-aware for Transferable Recommendation
- Tiger: Transferable Interest Graph Embedding for Domain-Level Zero-Shot Recommendation
- Towards Principled User-side Recommender Systems
- Towards Understanding the Overfitting Phenomenon of Deep Click-Through Rate Models
- Two-level Graph Path Reasoning for Conversational Recommendation with User Realistic Preference
- Long Short-Term Temporal Meta-learning in Online Recommendation
- Multi-Sparse-Domain Collaborative Recommendation via Enhanced Comprehensive Aspect Preference Learning
- RecGURU: Adversarial Learning of Generalized User Representations for Cross-Domain Recommendation
- Personalized Transfer of User Preferences for Cross-domain Recommendation
- Graph Collaborative Reasoning
- Modeling Scale-free Graphs with Hyperbolic Geometry for Knowledge-aware Recommendation
- Enumerating Fair Packages for Group Recommendations
- Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation
- CAN: Feature Co-Action Network for Click-Through Rate Prediction
- VAE++: Variational AutoEncoder for Heterogeneous One-Class Collaborative Filtering
- On Sampling Collaborative Filtering Datasets
- Triangle Graph Interest Network for Click-through Rate Prediction
- Show Me the Whole World: Towards Entire Item Space Exploration for Interactive Personalized Recommendations
- S-Walk: Accurate and Scalable Session-based Recommendation with Random Walks
- Choosing the Best of Both Worlds: Diverse and Novel Recommendations through Multi-Objective Reinforcement Learning
- Modeling Users’ Contextualized Page-wise Feedback for Click-Through Rate Prediction in E-commerce Search
- Toward Pareto Efficient Fairness-Utility Trade-off in Recommendation through Reinforcement Learning
- Supervised Advantage Actor-Critic for Recommender Systems
- Heterogeneous Global Graph Neural Networks for Personalized Session-based Recommendation
- Personalized Long-distance Fuel-efficient Route Recommendation Through Historical Trajectories Mining
- C2-CRS: Coarse-to-Fine Contrastive Learning for Conversational Recommender System
- Reinforcement Learning over Sentiment-Augmented Knowledge Graphs towards Accurate and Explainable Recommendation
- A Cooperative Neural Information Retrieval Pipeline with Knowledge Enhanced Automatic Query Reformulation
- Profiling the Design Space for Graph Neural Networks based Collaborative Filtering
- Towards Unbiased and Robust Causal Ranking for Recommender Systems
- Learning Multi-granularity Consecutive User Intent Unit for Session-based Recommendation
- Contrastive Meta Learning with Behavior Multiplicity for Recommendation
- Alleviating Cold-start Problem in CTR Prediction with A Variational Embedding Learning Framework
- A Model-Agnostic Causal Learning Framework for Recommendation using Search Data
- CAUSPref: Causal Preference Learning for Out-of-Distribution Recommendation
- FairGAN: GANs-based Fairness-aware Learning for Recommendations with Implicit Feedback
- Implicit User Awareness Modeling via Candidate Items for CTR Prediction in Search Ads
- Modeling User Behavior with Graph Convolution for Personalized Product Search
- Optimizing Rankings for Recommendation in Matching Markets
- PNMTA: A Pretrained Network Modulation and Task Adaptation Approach for User Cold-Start Recommendation
- Path Language Modeling over Knowledge Graphs for Explainable Recommendation
- Collaborative Filtering with Attribution Alignment for Review-based Non-overlapped Cross Domain Recommendation
- Differential Private Knowledge Transfer for Privacy-Preserving Cross-Domain Recommendation
- Will You Accept the AI Recommendation? Predicting Human Behavior in AI-Assisted Decision Making
- AutoField: Automating Feature Selection in Deep Recommender Systems
- CBR: Context Bias aware Recommendation for Debiasing User Modeling and Click Prediction
- Choice of Implicit Signal Matters: Accounting for User Aspirations in Podcast Recommendations
- Consensus Learning from Heterogeneous Objectives for One-Class Collaborative Filtering
- Cross Pairwise Ranking for Unbiased Item Recommendation
- Deep Unified Representation for Heterogeneous Recommendation
- Disentangling Long and Short-Term Interests for Recommendation
- Efficient Online Learning to Rank for Sequential Music Recommendation
- Fast Variational AutoEncoder with Inverted Multi-Index for Collaborative Filtering
- FeedRec: News Feed Recommendation with Various User Feedbacks
- Filter-enhanced MLP is All You Need for Sequential Recommendation
- FIRE: Fast Incremental Recommendation with Graph Signal Processing
- Generative Session-based Recommendation
- Graph Neural Transport Networks with Non-local Attentions for Recommender Systems
- Graph-based Extractive Explainer for Recommendations
- GSL4Rec: Session-based Recommendations with Collective Graph Structure Learning and Next Interaction Prediction
- Hypercomplex Graph Collaborative Filtering
- Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning
- Intent Contrastive Learning for Sequential Recommendation
- Learn over Past, Evolve for Future: Search-based Time-aware Recommendation with Sequential Behavior Data
- Learning Robust Recommenders through Cross-Model Agreement
- Learning to Augment for Casual User Recommendation
- MCL: Mixed-Centric Loss for Collaborative Filtering
- MINDSim: User Simulator for News Recommenders
- Modality Matches Modality: Pretraining Modality-Disentangled Item Representations for Recommendation
- Multiple Choice Questions based Multi-Interest Policy Learning for Conversational Recommendation
- Mutually-Regularized Dual Collaborative Variational Auto-encoder for Recommendation Systems
- Off-policy Learning over Heterogeneous Information for Recommendation
- Rating Distribution Calibration for Selection Bias Mitigation in Recommendations
- Re4: Learning to Re-contrast, Re-attend, Re-construct for Multi-interest Recommendation
- Sequential Recommendation via Stochastic Self-Attention
- Sequential Recommendation with Decomposed Item Feature Routing
- Stochastic-Expert Variational Autoencoder for Collaborative Filtering
- Towards Automatic Discovering of Deep Hybrid Network Architecture for Sequential Recommendation
- Unbiased Sequential Recommendation with Latent Confounders
- A Contrastive Sharing Model for Multi-Task Recommendation
- Accurate and Explainable Recommendation via Review Rationalization
- Comparative Explanations of Recommendations
- Recommendation Unlearning
- STAM: A Spatiotemporal Aggregation Method for Graph Neural Network-based Recommendation
- VisGNN: Personalized Visualization Recommendation via Graph Neural Networks
- Who to Watch Next: Two-side Interactive Networks for Live Broadcast Recommendation
- Causal Representation Learning for Out-of-Distribution Recommendation
- End-to-end Learning for Fair Ranking Systems
- Following Good Examples – Health Goal-Oriented Food Recommendation based on Behavior Data
- Link Recommendations for PageRank Fairness
- DCAF-BERT: A Distilled Cachable Adaptable Factorized Model For Improved Ads CTR Prediction
- DC-GNN: Decoupled Graph Neural Networks for Improving and Accelerating Large-Scale E-commerce Retrieval
- Simgrace: A Simple Framework for graph contrastive learning without data augmentation
- Towards Resolving Propensity Contradiction in Offline Recommender Learning
- MLP4Rec: A Pure MLP Architecture for Sequential Recommendations
- Discrete Listwise Personalized Ranking for Fast Top-N Recommendation with Implicit Feedback
- HCFRec: Hash Collaborative Filtering via Normalized Flow with Structural Consensus for Efficient Recommendation
- Enhancing Sequential Recommendation with Graph Contrastive Learning
- RecipeRec: A Heterogeneous Graph Learning Model for Recipe Recommendation
- Modeling Spatio-temporal Neighbourhood for Personalized Point-of-interest Recommendation
- Heterogeneous Interactive Snapshot Network for Review-Enhanced Stock Profiling and Recommendation
- Next Point-of-Interest Recommendation with Inferring Multi-step Future Preferences
- Poisoning Deep Learning Based Recommender Model in Federated Learning Scenarios
- Self-supervised Graph Neural Networks for Multi-behavior Recommendation
- Trading Hard Negatives and True Negatives: A Debiased Contrastive Collaborative Filtering Approach
- Estimating and Penalizing Induced Preference Shifts in Recommender Systems
- Learning from a Learning User for Optimal Recommendations
- DCAH: Search Behavior Prediction: A Hypergraph Perspective (WSDM'23)
- CLUE: Scaling Law for Recommendation Models: Towards General-purpose User Representations (AAAI'23)
- PinnerFormer: PinnerFormer: Sequence Modeling for User Representation at Pinterest (KDD'22)
- ItemSage: ItemSage: Learning Product Embeddings for Shopping Recommendations at Pinterest (KDD'22)
- DuoRec: Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation (WSDM'22)
- FMLP-Rec: FilterEnhanced MLP is All You Need for Sequential Recommendation (WWW'22)
- CML: Contrastive Meta Learning with Behavior Multiplicity for Recommendation (WSDM'22)
- Tiger: Tiger: Transferable Interest Graph Embedding for Domain-Level Zero-Shot Recommendation (CIKM'22)
- AiRS: AiRS: A Large-Scale Recommender System at NAVER News (ICDE'22)
- CoRGi: CORGI: Content-Rich Graph Neural Networks with Attention (KDD'22)
- SimGCL: Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation (SIGIR'22)
- HCCF: Hypergraph Contrastive Collaborative Filtering (SIGIR'22)
- NCL: Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning (WWW'22)
- SHT: Self-Supervised Hypergraph Transformer for Recommender Systems (KDD'22)
- SimGRACE: Simgrace: A Simple Framework for graph contrastive learning without data augmentation (WWW'22)
- AutoGCL: Autogcl: Automated graph contrastive learning via learnable view generators (AAAI'22)
- SAIL: SAIL: Self-Augmented Graph Contrastive Learning (AAAI'22)
- UniSRec: Towards Universal Sequence Representation Learning for Recommender Systems (KDD'22)
- MAIL: Zero Shot on the Cold-Start Problem: Model-Agnostic Interest Learning for Recommender Systems (CIKM'21)
- Transformer4Rec: Transformers4Rec: Bridging the Gap between NLP and Sequential / Session-Based Recommendation (Recsys'21)
- NGF: Neural graph filtering for context-aware recommendation (ACML'21)
- SGL: Self-supervised Graph Learning for Recommendation (SIGIR'21)
- MHCN: Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation (WWW'21)
- DHCN: Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation (AAAI'21)
- MINCE: Memory Augmented MultiInstance Contrastive Predictive Coding for Sequential Recommendation (ICDM'21)
- SEPT: Socially-Aware Self-Supervised Tri-Training for Recommendation (KDD'21)
- BUIR: Bootstrapping User and Item Representations for One-Class Collaborative Filtering (SIGIR'21)
- UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation (CIKM'21)
- MixGCF: MixGCF: An Improved Training Method for Graph Neural Network-based Recommender Systems (KDD'21)
- SERec: An Efficient and Effective Framework for Session-based Social Recommendation (WSDM'21)
- CL4SRec: Contrastive Learning for Sequential Recommendation (SIGIR'21)
- GCA: Graph Contrastive Learning with Adaptive Augmentation (WWW'21)
- PinnerSage: PinnerSage: Multi-Modal User Embedding Framework for Recommendations at Pinterest (KDD'20)
- TAFA: TAFA: Two-headed attention fused autoencoder for context-aware recommendations (Recsys'20)
- MBCN: Multi-Branch Convolutional Network for Context-Aware Recommendation (SIGIR'20)
- ENSFM: Efficient Non-Sampling Factorization Machines for Optimal Context-Aware Recommendation (WWW'20)
- S3-Rec: S3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization (CIKM'20)
- DMR: Deep Match to Rank Model for Personalized Click-Through Rate Prediction (AAAI'20)
- EHCF: Efficient heterogeneous collaborative filtering without neg-ative sampling for recommendation (AAAI'20)
- SCE-GNN: Global Context Enhanced Graph Neural Networks for Session-based Recommendation (SIGIR'20)
- SSG: Set-Sequence-Graph: A Multi-View Approach Towards Exploiting Reviews for Recommendation (CIKM'20)
- SML: Symmetric Metric Learning with Adaptive Margin for Recommendation (AAAI'20)
- KHGT: Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation (AAAI'20)
- LCF: Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters (PMLR'20)
- SEE-PT: SEE-PT: Sequential recommendation via personalized transformer (RecSys'20)
- LightGCN: LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation (SIGIR'20)
- MBGCN: Multi-behavior Recommendation with Graph Convolutional Networks (SIGIR'20)
- MA-GNN: Memory Augmented Graph Neural Networks for Sequential Recommendation (AAAI'20)
- GCCF: Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach (AAAI'20)
- HyRec: Next-item Recommendation with Sequential Hypergraphs (SIGIR'20)
- DGCF: Disentangled Graph Collaborative Filtering (SIGIR'20)
- GRACE: Deep Graph Contrastive Representation Learning (arXiv'20)
- LLAE: From Zero-Shot Learning to Cold-Start Recommendation (AAAI'19)
- MetaEmb: Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings (SIGIR'19)
- NMTR: Neural Multi-Task Recommendation from Multi-Behavior Data (ICDE'19)
- NGCF: Neural Graph Collaborative Filtering (SIGIR'19)
- BERT4Rec: BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer (CIKM'19)
- KGAT: KGAT: Knowledge Graph Attention Network for Recommendation (KDD'19)
- KGCN: Knowledge Graph Convolutional Networks for Recommender Systems (WWW'19)
- GraphRec: Graph Neural Networks for Social Recommendation (WWW'19)
- NARRE: Neural Attentional Rating Regression with Review-level Explanations (WWW'19)
- METAS: Action Space Learning for Heterogeneous User Behavior Prediction (IJCAI'19)
- SR-GNN: Session-based recommendation with graph neural networks (AAAI'19)
- SelCa: Recommender System Using Sequential and Global Preference via Attention Mechanism and Topic Modeling (CIKM'19)
- MMGCN: MMGCN: Multi-modal Graph Convolution Network for Personalized Recommendation of Micro-video (MM'19)
- Caser: Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding (WSDM'18)
- PinSage: Graph Convolutional Neural Networks for Web-Scale Recommender Systems (KDD'18)
- HIN: Adaptive Implicit Friends Identification over Heterogeneous Network for Social Recommendation (CIKM'18)
- VAE: Variational Autoencoders for Collaborative Filtering (WWW'18)
- triple2vec: Representing and Recommending Shopping Baskets with Complementarity, Compatibility and Loyalty (CIKM'18)
- GC-MC: Graph Convolutional Matrix Completion (KDD'18)
- SASRec: Self-Attentive Sequential Recommendation (ICDM'18)
- SDNets: Adversarial Distillation for Efficient Recommendation with External Knowledge (TOIS'18)
- AIN: An Attentive Interaction Network for Context-aware Recommendation (CIKM'18)
- ConvNCF: Outer Product-based Neural Collaborative Filtering (IJCAI'18)
- STAMP: STAMP: shortterm attention/memory priority model for session-based recommendation (KDD'18)
- A3CF: An Adaptive Aspect Attention Model for Rating Prediction (IJCAI'18)
- TransNet: TransNets: Learning to Transform for Recommendation (Recsys'17)
- DeepCoNN: Joint Deep Modeling of Users and Items Using Reviews for Recommendation (WSDM'17)
- ACF: Attentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-Level Attention (SIGIR'17)
- CML: Collaborative Metric Learning (WWW'17)
- NMF: Neural Factorization Machines for Sparse Predictive Analytics (SIGIR'17)
- DMF: Deep matrix factorization models for recommender systems (IJCAI'17)
- NARM: Neural attentive session-based recommendation (CIKM'17)
- NCF: Neural Collaborative Filtering (WWW'17)
- GRU: Sequential User-based Recurrent Neural Network Recommendations (RecSys'17)
- CDAE: Collaborative Denoising Auto-Encoders for Top-N Recommender Systems (WSDM'16)
- DREAM: A Dynamic Recurrent Model for Next Basket Recommendation (SIGIR'16)
- ConvMF: Convolutional Matrix Factorization for Document Context-Aware Recommendation (RecSys'16)
- eALS: Fast Matrix Factorization for Online Recommendation with Implicit Feedback (SIGIR'16)
- GRU4Rec: Session-based Recommendations with Recurrent Neural Networks (ICLR'16)
- AutoRec: AutoRec: Autoencoders Meet Collaborative Filtering (WWW'15)
- CDL: Collaborative Deep Learning for Recommender Systems (KDD'15)
- CSLIM: Deviation-Based Contextual SLIM Recommenders (CIKM'14)
- LogisticMF: Logistic Matrix Factorization for Implicit Feedback (NeurIPS'14)
- HFT: Hidden factors and hidden topics: understanding rating dimensions with review text (Recsys'13)
- CTR: Collaborative topic modeling for recommending scientific articles (KDD'11)
- SLIM: SLIM: Sparse Linear Methods for Top-N Recommender Systems (ICDM'11)
- MF: Matrix factorization techniques for recommender systems (MC'09)
- BPR: BPR: Bayesian Personalized Ranking from Implicit Feedback (UAI'09)
- SoRec: SoRec: Social Recommendation Using Probabilistic Matrix Factorization (CIKM'08)
- ALS: Collaborative Filtering for Implicit Feedback Datasets (ICDM'08)
- RBM: Restricted Boltzmann Machines for Collaborative Filtering (ICML'07)
- Item-Base CF: Item-based top-N recommendation algorithms (TOIS'04)
- Self-supervised Learning for Large-scale Item Recommendations (CIKM'21)
- Disentangled Self-Supervision in Sequential Recommenders (KDD'20)
- The YouTube video recommendation system (Recsys'16)
- Multiverse Recommendation: N-dimensional Tensor Factorization for Context-aware Collaborative Filtering (Recsys'10)
- The Netflix prize (KDD'07)
- Amazon.com recommendations: item-to-item collaborative filtering (MIC'03)
- Item-based collaborative filtering recommendation algorithms (WWW'01)
- Learning Collaborative Information Filters (AAAI'98)
- GroupLens: An Open Architecture for Collaborative Filtering of Netnews (CSCW'94)
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