推荐系统领域对比学习和数据增强论文及代码集锦

对比学习和数据增强是近年各领域关注度较高的研究方向，在推荐系统领域也是如此，并取得了众多成果。本文汇总了推荐系统领域对比学习和数据增强的最新论文和代码，涵盖 SIGIR、SIGKDD、RecSys、CIKM、AAAI、WSDM、WWW等会议和TKDE、TMM等期刊共计85篇论文，本次整理以长文和研究性论文为主，也包含部分短文和工业论文。欢迎大家在评论区或Github仓库提出建议或补充论文。具体论文和代码链接请见Github仓库。部分论文来自RUC AI Box的《800 篇顶会论文纵览推荐系统的前沿进展》，感谢他们的辛苦整理。

仓库地址

KingGugu/Awesome-Contrastive-Learning-and-Data-Augmentation-RS-Paper-Code

目录

Survey/Tutorial（综述/教程） Total Papers: 2
Only Data Augmentation（只有数据增强） Total Papers: 14
Graph Models with CL（序列推荐与对比学习）Total Papers: 27
Sequential Models with CL（图推荐与对比学习）Total Papers: 20
Other Tasks with CL（其他推荐任务与对比学习）Total Papers: 22

Survey/Tutorial

1.Self-Supervised Learning for Recommender Systems A Survey (Survey)
TKDE 2022, [PDF], [Code]
2.Self-Supervised Learning in Recommendation: Fundamentals and Advances (Tutorial)
WWW 2022, [Web]

Only Data Augmentation

1.Enhancing Collaborative Filtering with Generative Augmentation (CF + GAN + DA)
KDD 2019, [PDF]
2.Future Data Helps Training Modeling Future Contexts for Session-based Recommendation (Session + DA)
WWW 2020, [PDF], [Code]
3.Augmenting Sequential Recommendation with Pseudo-Prior Items via Reversely Pre-training Transformer (Sequential + DA)
SIGIR 2021, [PDF], [Code]
4.Self-Knowledge Distillation with Bidirectional Chronological Augmentation of Transformer for Sequential Recommendation (Sequential + DA)
arXiv 2022, [PDF], [Code]
5.Counterfactual Data-Augmented Sequential Recommendation (Sequential + Counterfactual + DA)
SIGIR 2021, [PDF]
6.CauseRec: Counterfactual User Sequence Synthesis for Sequential Recommendation (Sequential + Counterfactual + DA)
SIGIR 2021, [PDF]
7.Explicit Counterfactual Data Augmentation for Recommendation (Sequential + Counterfactual + DA)
WSDM 2023
8.Effective and Efficient Training for Sequential Recommendation using Recency Sampling (Sequential + DA)
RecSys 2022, [PDF]
9.Data Augmentation Strategies for Improving Sequential Recommender Systems (Sequential + DA)
arXiv 2022, [PDF], [Code]
10.Learning to Augment for Casual User Recommendation (Sequential + DA)
WWW 2022, [PDF]
11.Recency Dropout for Recurrent Recommender Systems (RNN + DA)
arXiv 2022, [PDF]
12.Improved Recurrent Neural Networks for Session-based Recommendations (RNN + DA)
DLRS 2016, [PDF]
13.Bootstrapping User and Item Representations for One-Class Collaborative Filtering (CF + Graph + DA)
SIGIR 2021, [PDF], [Code]
14.MixGCF: An Improved Training Method for Graph Neural Network-based Recommender Systems (Graph + DA)
KDD 2021, [PDF], [Code]

Graph Models with CL

1.Self-supervised Graph Learning for Recommendation (Graph + CL + DA)
SIGIR 2021, [PDF], [Code]
2.Contrastive Graph Structure Learning via Information Bottleneck for Recommendation (Graph + CL)
NeurIPS 2022, [PDF], [Code]
3.Are graph augmentations necessary? simple graph contrastive learning for recommendation (Graph + CL)
SIGIR 2022, [PDF], [Code]
4.XSimGCL: Towards Extremely Simple Graph Contrastive Learning for Recommendation (Graph + CL)
TKDE 2022, [PDF], [Code]
5.Intent-aware Multi-source Contrastive Alignment for Tag-enhanced Recommendation (Graph + CL + DA)
arXiv 2022, [PDF]
6.DisenPOI: Disentangling Sequential and Geographical Influence for Point-of-Interest Recommendation (POI Rec, Graph + CL + DA)
WSDM 2023, [PDF], [Code]
7.An MLP-based Algorithm for Efficient Contrastive Graph Recommendations (Short paper, Graph + CL + DA)
SIGIR 2022, [PDF]
8.A Review-aware Graph Contrastive Learning Framework for Recommendation (Graph + CL + DA)
SIGIR 2022, [PDF], [Code]
9.Simple Yet Effective Graph Contrastive Learning for Recommendation (Graph + CL + DA)
ICLR 2023, Under double-blind review, [PDF], [Code]
10.Contrastive Meta Learning with Behavior Multiplicity for Recommendation (Graph + CL + DA)
WSDM 2022, [PDF], [Code]
11.Disentangled Contrastive Learning for Social Recommendation (Short paper, Graph + CL + DA)
CIKM 2022, [PDF]
12.Improving Knowledge-aware Recommendation with Multi-level Interactive Contrastive Learning (Graph + CL)
CIKM 2022, [PDF], [Code]
13.Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System (Graph + CL)
SIGIR 2022, [PDF], [Code]
14.Knowledge Graph Contrastive Learning for Recommendation (Graph + DA + CL)
SIGIR 2022, [PDF], [Code]
15.Temporal Knowledge Graph Reasoning with Historical Contrastive Learning (Graph + CL)
IJCAI 2022, [PDF], [Code]
16.Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation (Graph + SSL)
WWW 2021, [PDF], [Code]
17.SAIL: Self-Augmented Graph Contrastive Learning (Graph + CL)
AAAI 2022, [PDF]
18.Predictive and Contrastive: Dual-Auxiliary Learning for Recommendation (Graph + CL)
arXiv 2022, [PDF]
19.Socially-Aware Self-Supervised Tri-Training for Recommendation (Graph + CL)
KDD 2021, [PDF], [Code]
20.Predictive and Contrastive: Dual-Auxiliary Learning for Recommendation (Graph + CL)
arXiv 2022, [PDF]
21.Multi-Behavior Dynamic Contrastive Learning for Recommendation (Graph + CL)
arXiv 2022, [PDF]
22.Self-Augmented Recommendation with Hypergraph Contrastive Collaborative Filtering (Graph + CL)
SIGIR 2022, [PDF], [Code]
23.Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning (Graph + CF + CL)
WWW 2022, [PDF], [Code]
24.Semi-deterministic and Contrastive Variational Graph Autoencoder for Recommendation (Graph + CL)
CIKM 2021, [PDF], [Code]
25.Hypergraph Contrastive Collaborative Filtering (Graph + CF + CL + DA)
SIGIR 2022, [PDF], [Code]
26.Graph Structure Aware Contrastive Knowledge Distillation for Incremental Learning in Recommender Systems (Short paper, Graph + CL)
CIKM 2021, [PDF], [Code]
27.Double-Scale Self-Supervised Hypergraph Learning for Group Recommendation (Group Rec, Graph + CL + DA)
CIKM 2021, [PDF], [Code]

Sequential Models with CL

1.Uniform Sequence Better: Time Interval Aware Data Augmentation for Sequential Recommendation (Sequential + CL + DA)
AAAI 2023, [PDF], [Code]
2.Contrastive Learning for Sequential Recommendation (Sequential + CL + DA)
SIGIR 2021, [PDF], [Code]
3.Contrastive Self-supervised Sequential Recommendation with Robust Augmentation (Sequential + CL + DA)
SIGIR 2021, [PDF], [Code]
4.Learnable Model Augmentation Self-Supervised Learning for Sequential Recommendation (Sequential + CL + DA)
arXiv 2022, [PDF]
5.S3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization (Sequential + CL + DA)
CIKM 2020, [PDF], [Code]
6.Contrastive Curriculum Learning for Sequential User Behavior Modeling via Data Augmentation (Sequential + CL + DA)
CIKM 2021, [PDF] , [Code]
7.Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation (Sequential + CL + DA)
WSDM 2022, [PDF], [Code]
8.Memory Augmented Multi-Instance Contrastive Predictive Coding for Sequential Recommendation (Sequential + CL + DA)
arXiv 2021, [PDF]
9.Contrastive Learning with Bidirectional Transformers for Sequential Recommendation (Sequential + CL + DA)
CIKM 2022, [PDF], [Code]
10.ContrastVAE: Contrastive Variational AutoEncoder for Sequential Recommendation (Sequential + CL + DA)
CIKM 2022, [PDF], [Code]
11.Temporal Contrastive Pre-Training for Sequential Recommendation (Sequential + CL + DA)
CIKM 2022, [PDF], [Code]
12.Multi-level Contrastive Learning Framework for Sequential Recommendation (Graph + Sequential + CL)
CIKM 2022, [PDF]
13.Equivariant Contrastive Learning for Sequential Recommendation (Sequential + DA + CL)
arXiv 2022, [PDF]
14.Explanation Guided Contrastive Learning for Sequential Recommendation (Sequential + DA + CL)
CIKM 2022, [PDF], [Code]
15.Intent Contrastive Learning for Sequential Recommendation (Sequential + DA + CL)
WWW 2022, [PDF], [Code]
16.Dual Contrastive Network for Sequential Recommendation (Short paper, Sequential + CL)
SIGIR 2022, [PDF]
17.Dual Contrastive Network for Sequential Recommendation with User and Item-Centric Perspectives (Sequential + CL)
arXiv 2022, [PDF]
18.Enhancing Sequential Recommendation with Graph Contrastive Learning (Sequential + Graph + CL + DA)
IJCAI 2022, [PDF], [Code]
19.Disentangling Long and Short-Term Interests for Recommendation (Sequential + Graph + CL)
WWW 2022, [PDF], [Code]
20.Hyperbolic Hypergraphs for Sequential Recommendation (Sequential + Graph + CL + DA)
CIKM 2021, [PDF], [Code]

Other Tasks with CL

1.CL4CTR: A Contrastive Learning Framework for CTR Prediction (CTR + CL)
WSDM 2023, [PDF], [Code]
2.CCL4Rec: Contrast over Contrastive Learning for Micro-video Recommendation (Micro-video + CL)
arXiv 2022, [PDF]
3.Re4: Learning to Re-contrast, Re-attend, Re-construct for Multi-interest Recommendation (Multi-interest + CL)
WWW 2022, [PDF], [Code]
4.Interventional Recommendation with Contrastive Counterfactual Learning for Better Understanding User Preferences (Counterfactual + DA + CL)
arXiv 2022, [PDF]
5.Multi-granularity Item-based Contrastive Recommendation (Industry + CL)
arXiv 2022, [PDF]
6.Improving Micro-video Recommendation via Contrastive Multiple Interests (Short paper, Micro-video + CL)
SIGIR 2022, [PDF]
7.Exploiting Negative Preference in Content-based Music Recommendation with Contrastive Learning (Music Rec + CL)
RecSys 2022, [PDF], [Code]
8.Self-supervised Learning for Large-scale Item Recommendations (Industry + CL + DA)
CIKM 2021, [PDF]
9.CrossCBR: Cross-view Contrastive Learning for Bundle Recommendation (Bundle Rec + CL)
KDD 2023, [PDF], [Code]
10.Contrastive Learning for Cold-start Recommendation (Short paper, Cold Start + CL)
ACM MM (ACM International Conference on Multimedia) 2021, [PDF], [Code]
11.Socially-aware Dual Contrastive Learning for Cold-Start Recommendation (Short paper, Cold Start + CL)
SIGIR 2022, [PDF]
12.Multi-modal Graph Contrastive Learning for Micro-video Recommendation (Short paper, Cold Start + Graph + CL)
SIGIR 2022, [PDF]
13.Self-supervised Learning for Multimedia Recommendation (Multimedia Rec + Graph + DA + CL)
TMM (IEEE Transactions on Multimedia) 2022, [PDF], [Code]
14.SelfCF: A Simple Framework for Self-supervised Collaborative Filtering (CF + Graph + DA + CL)
ACM MM (ACM International Conference on Multimedia) 2021, [PDF], [Code]
15.Trading Hard Negatives and True Negatives:A Debiased Contrastive Collaborative Filtering Approach (CF + CL)
IJCAI 2022, [PDF]
16.The World is Binary: Contrastive Learning for Denoising Next Basket Recommendation (Next Basket + CL)
SIGIR 2021, [PDF]
17.MIC: Model-agnostic Integrated Cross-channel Recommender (Industry + CL + DA)
CIKM 2022, [PDF]
18.A Contrastive Sharing Model for Multi-Task Recommendation (Multi-Task + CL)
WWW 2022, [PDF]
19.C2-CRS: Coarse-to-Fine Contrastive Learning for Conversational Recommender System (Conversational Rec + CL)
WSDM 2022, [PDF], [Code]
20.Contrastive Cross-domain Recommendation in Matching (Cross-domain Rec + DA + CL)
KDD 2022, [PDF], [Code]
21.Contrastive Cross-domain Recommendation in Matching (Cross-domain Rec + Sequential + CL)
CIKM 2022, [PDF], [Code]
22.Prototypical Contrastive Learning and Adaptive Interest Selection for Candidate Generation in Recommendations (Short Paper, Industry + CL + DA)
CIKM 2022, [PDF], [Code]