SIGIR2021推荐系统论文集锦

嘿，记得给“机器学习与推荐算法”添加星标

---

---

第44届国际信息检索研究和发展大会（SIGIR）将于2021年7月11-15日在线上举行（目前正在进行中）。此次大会共收到了720篇长文投稿，录用151篇，长文录取率21%（去年的录取率为26.4%）；共收到了526篇短文投稿，录用145篇，短文录取率27%（去年的录取率为30%）。

正因为推荐与搜索是解决信息过载的两种有效途径，因此虽然是关于检索的会议，但推荐系统占据了很大比例，与信息搜索不相上下。本文对推荐系统相关的论文进行了整理。为了方便查看与了解，我们主要将其分为了以下几类：Collaborative Filtering、Privacy&Security in RS、Sequential RS、Graph-based RS、Explainable RS、Conversational RS、News RS、Social RS、Cross-domain RS、Attention based RS、Fair RS。

另外，以上分类仁者见仁，智者见智，目的是给大家一个相对清晰的结构。如果看的不过瘾，除了按照以上分类来进行展示外，我们还给出了按照长文和短文进行粗粒度分类的论文列表，以供大家进行更加全面的浏览相关idea以及按照自己的标准来进行分类。需要注意的是，文本涉及的论文中大部分提供了原论文的PDF阅读链接与源码链接。P.S. 更加详细与官方的论文列表如下：

https://sigir.org/sigir2021/accepted-papers/

Collaborative Filtering

• Bootstrapping User and Item Representations for One-Class Collaborative Filtering --https://arxiv.org/abs/2105.06323

• Neural Graph Matching based Collaborative Filtering (PDF)--https://arxiv.org/abs/2105.04067 (Code)--https://github.com/ruizhang-ai/GMCF_Neural_Graph_Matching_based_Collaborative_Filtering

• Enhanced Graph Learning for Collaborative Filtering via Mutual Information Maximization

Privacy & Security in RS

• A Study of Defensive Methods to Protect Visual Recommendation Against Adversarial Manipulation of Images --http://sisinflab.poliba.it/publications/2021/ADDMM21/SIGIR2021_A_Study_of_Defensive_Methods_to_Protect_Visual_Recommendation_Against_Adversarial_Manipulation_of_Images.pdf

• Fight Fire with Fire: Towards Robust Recommender Systems via Adversarial Poisoning Training

Sequential Recommendation

• Category-aware Collaborative Sequential Recommendation

• Sequential Recommendation with Graph Convolutional Networks

• StackRec: Efficient Training of Very Deep Sequential Recommender Models by Iterative Stacking  (PDF)--https://arxiv.org/abs/2012.07598 (Code)--https://github.com/wangjiachun0426/StackRec

• Counterfactual Data-Augmented Sequential Recommendation

• CauseRec: Counterfactual User Sequence Synthesis for Sequential Recommendation

Session-based Recommendation

• Dual Attention Transfer in Session-based Recommendation with Multi Dimensional Integration

• Unsupervised Proxy Selection for Session-based Recommender Systems

• The World is Binary: Contrastive Learning for Denoising Next Basket Recommendation  --https://github.com/QYQ-bot/CLEA

Graph-based Recommendation

• Sequential Recommendation with Graph Convolutional Networks

• Structured Graph Convolutional Networks with Stochastic Masks for Recommender Systems

• Unified Conversational Recommendation Policy Learning via Graph-based Reinforcement Learning  --https://arxiv.org/abs/2105.09710

• Neural Graph Matching based Collaborative Filtering (PDF)--https://arxiv.org/abs/2105.04067 (Code)--https://github.com/ruizhang-ai/GMCF_Neural_Graph_Matching_based_Collaborative_Filtering

• Joint Knowledge Pruning and Recurrent Graph Convolution for News Recommendation  --https://yuh-yang.github.io/resources/kopra.pdf

• Privileged Graph Distillation for Cold-start Recommendation  --https://arxiv.org/abs/2105.14975

• Self-supervised Graph Learning for Recommendation  (PDF)--https://arxiv.org/abs/2010.10783 (Code)--https://github.com/wujcan/SGL

• Graph Meta Network for Multi-Behavior Recommendation with Interaction Heterogeneity and Diversity

• Enhanced Graph Learning for Collaborative Filtering via Mutual Information Maximization

Explainable Recommendation

• ReXPlug: Explainable Recommendation using Plug-and-Play Language Model

• User-Centric Path Reasoning towards Explainable Recommendation

Conversational Recommendation

• Unified Conversational Recommendation Policy Learning via Graph-based Reinforcement Learning  --https://arxiv.org/abs/2105.09710

• Learning to Ask Appropriate Questions in Conversational Recommendation

• Comparison-based Conversational Recommender System with Relative Bandit Feedback

News Recommendation

• Personalized News Recommendation with Knowledge-aware News Interactions --https://arxiv.org/abs/2104.10083

• Joint Knowledge Pruning and Recurrent Graph Convolution for News Recommendation  --https://yuh-yang.github.io/resources/kopra.pdf

Social Recommendation

• Social Recommendation with Implicit Social Influence

• ConsisRec: Enhancing GNN for Social Recommendation via Consistent Neighbor Aggregation  (PDF)--https://arxiv.org/abs/2105.02254 (Code)--https://github.com/YangLiangwei/ConsisRec

Cross-domain Recommendation

• Federated Collaborative Transfer for Cross-Domain Recommendation

• Learning Domain Semantics and Cross-Domain Correlations for Paper Recommendation  --http://splab.sdu.edu.cn/download/paper/SIGIR-Cross-210501.pdf

Attention & Transformer & BERT

• Dual Attention Transfer in Session-based Recommendation with Multi Dimensional Integration

• Package Recommendation with Intra- and Inter-Package Attention Networks  --http://shichuan.org/doc/108.pdf

Self-supervised & Contrasive Learning

• The World is Binary: Contrastive Learning for Denoising Next Basket Recommendation  --https://github.com/QYQ-bot/CLEA

• Self-supervised Graph Learning for Recommendation  (PDF)--https://arxiv.org/abs/2010.10783 (Code)--https://github.com/wujcan/SGL

Cold-start Problem

• FORM: Follow the Online Regularized Meta-Leader for Cold-Start Recommendation

• Privileged Graph Distillation for Cold-start Recommendation  --https://arxiv.org/abs/2105.14975

• Learning to Warm Up Cold Item Embeddings for Cold-start Recommendation with Meta Scaling and Shifting Networks  --https://arxiv.org/abs/2105.04790

• Fairness among New Items in Cold Start Recommender Systems  --http://people.tamu.edu/~zhuziwei/pubs/Ziwei_SIGIR_2021.pdf

Bias & Fairness

• AutoDebias: Learning to Debias for Recommendation  (PDF)--https://arxiv.org/abs/2105.04170 (Code)--https://github.com/DongHande/AutoDebias

• Personalized Counterfactual Fairness in Recommendation

• Mitigating Sentiment Bias for Recommender Systems

• TFROM: A Two-sided Fairness-Aware Recommendation Model for Both Customers and Providers  --https://arxiv.org/abs/2104.09024

• Causal Intervention for Leveraging Popularity Bias in Recommendation  (PDF)--http://staff.ustc.edu.cn/~hexn/papers/sigir21-PDA.pdf (Code)--https://github.com/zyang1580/PDA

• Fairness among New Items in Cold Start Recommender Systems  --http://people.tamu.edu/~zhuziwei/pubs/Ziwei_SIGIR_2021.pdf

---

以上为按照推荐系统研究问题进行分类展示，以下将列举出SIGIR2021上关于接收的关于推荐系统大领域的长文和短文的论文列表，供大家有选择的阅读。另外，大部分提供了论文的PDF阅读链接与源码链接。

Long Papers

[1] A Study of Defensive Methods to Protect Visual Recommendation Against Adversarial Manipulation of Images

http://sisinflab.poliba.it/publications/2021/ADDMM21/SIGIR2021_A_Study_of_Defensive_Methods_to_Protect_Visual_Recommendation_Against_Adversarial_Manipulation_of_Images.pdf

[2] On Interpretation and Measurement of Soft Attributes for Recommendation

https://storage.googleapis.com/pub-tools-public-publication-data/pdf/9466caf5c19edb6fb95cadb322baf4912c1a1866.pdf

[3] Category-aware Collaborative Sequential Recommendation

[4] DEKR: Description Enhanced Knowledge Graph for Machine Learning Method Recommendation

[5] Sequential Recommendation with Graph Convolutional Networks

[6] Dual Attention Transfer in Session-based Recommendation with Multi Dimensional Integration

[7] Structured Graph Convolutional Networks with Stochastic Masks for Recommender Systems

[8] AutoDebias: Learning to Debias for Recommendation

https://arxiv.org/abs/2105.04170)]

https://github.com/DongHande/AutoDebias

[9] Set2setRank: Collaborative Set to Set Ranking for Implicit Feedback based Recommendation

https://arxiv.org/abs/2105.07377

[10] Learning Recommender Systems with Implicit Feedback via Soft Target Enhancement

[11] Unsupervised Proxy Selection for Session-based Recommender Systems

[12] Unified Conversational Recommendation Policy Learning via Graph-based Reinforcement Learning

https://arxiv.org/abs/2105.09710

[13] ReXPlug: Explainable Recommendation using Plug-and-Play Language Model

[14] Bootstrapping User and Item Representations for One-Class Collaborative Filtering

https://arxiv.org/abs/2105.06323

[15] Package Recommendation with Intra- and Inter-Package Attention Networks

http://shichuan.org/doc/108.pdf

[16] When and Whom to Collaborate with in a Changing Environment: A Collaborative Dynamic Bandit Solution

https://arxiv.org/abs/2104.07150

[17] Path-based Deep Network for Candidate Item Matching in Recommenders

https://arxiv.org/abs/2105.08246

[18] New Insights into Metric Optimization for Ranking-based Recommendation

https://julian-urbano.info/files/publications/046-new-insights-metric-optimization-ranking-based-recommendation.pdf

https://github.com/roger-zhe-li/sigir21-newinsights

[19] Personalized Counterfactual Fairness in Recommendation

[20] Enhancing Domain-Level and User-Level Adaptivity in Diversified Recommendation

[21] Mitigating Sentiment Bias for Recommender Systems

[22] Federated Collaborative Transfer for Cross-Domain Recommendation

[23] Standing in Your Shoes: External Assessments for Personalized Recommender Systems

https://staff.fnwi.uva.nl/m.derijke/wp-content/papercite-data/pdf/lu-2021-standing.pdf

[24] Personalized News Recommendation with Knowledge-aware News Interactions

https://arxiv.org/abs/2104.10083

[25] The World is Binary: Contrastive Learning for Denoising Next Basket Recommendation

https://github.com/QYQ-bot/CLEA

[26] A Guided Learning Approach for Item Recommendation via Surrogate Loss Learning

[27] Learning to Ask Appropriate Questions in Conversational Recommendation

[28] Neural Graph Matching based Collaborative Filtering

https://arxiv.org/abs/2105.04067

https://github.com/ruizhang-ai/GMCF_Neural_Graph_Matching_based_Collaborative_Filtering

[29] FORM: Follow the Online Regularized Meta-Leader for Cold-Start Recommendation

[30] User-Centric Path Reasoning towards Explainable Recommendation

[31] Joint Knowledge Pruning and Recurrent Graph Convolution for News Recommendation

PDF](https://yuh-yang.github.io/resources/kopra.pdf

[32] StackRec: Efficient Training of Very Deep Sequential Recommender Models by Iterative Stacking

https://arxiv.org/abs/2012.07598

https://github.com/wangjiachun0426/StackRec

[33] Privileged Graph Distillation for Cold-start Recommendation

https://arxiv.org/abs/2105.14975

[34] Clicks can be Cheating: Counterfactual Recommendation for Mitigating Clickbait Issue

https://arxiv.org/abs/2009.09945

[35] Counterfactual Data-Augmented Sequential Recommendation

[36] Fight Fire with Fire: Towards Robust Recommender Systems via Adversarial Poisoning Training

[37] Self-supervised Graph Learning for Recommendation

https://arxiv.org/abs/2010.10783

https://github.com/wujcan/SGL

[38] TFROM: A Two-sided Fairness-Aware Recommendation Model for Both Customers and Providers

https://arxiv.org/abs/2104.09024

[39] Graph Meta Network for Multi-Behavior Recommendation with Interaction Heterogeneity and Diversity

[40] Learning Domain Semantics and Cross-Domain Correlations for Paper Recommendation

http://splab.sdu.edu.cn/download/paper/SIGIR-Cross-210501.pdf

[41] Comparison-based Conversational Recommender System with Relative Bandit Feedback

[42] Enhanced Graph Learning for Collaborative Filtering via Mutual Information Maximization

[43] CauseRec: Counterfactual User Sequence Synthesis for Sequential Recommendation

[44] Counterfactual Reward Modification for Streaming Recommendation with Delayed Feedback

[45] Causal Intervention for Leveraging Popularity Bias in Recommendation

http://staff.ustc.edu.cn/~hexn/papers/sigir21-PDA.pdf

https://github.com/zyang1580/PDA

[46] UGRec: Modeling Directed and Undirected Relations for Recommendation

https://arxiv.org/abs/2105.04183

[47] Learning to Warm Up Cold Item Embeddings for Cold-start Recommendation with Meta Scaling and Shifting Networks

https://arxiv.org/abs/2105.04790

[48] Fairness among New Items in Cold Start Recommender Systems

http://people.tamu.edu/~zhuziwei/pubs/Ziwei_SIGIR_2021.pdf

---

Short Papers

[1] Variational Autoencoders for Top-K Recommendation with Implicit Feedback

[2] Motif-aware Sequential Recommendation

[3] Lighter and Better: Low-Rank Decomposed Self-Attention Networks for Next-Item Recommendation

https://www.microsoft.com/en-us/research/uploads/prod/2021/05/LighterandBetter_Low-RankDecomposedSelf-AttentionNetworksforNext-ItemRecommendation.pdf

[4] The Winner Takes it All: Geographic Imbalance and Provider (Un)fairness in Educational Recommender Systems

[5] RMBERT:News Recommendation via Recurrent Reasoning Memory Network over BERT

[6] Entangled Bidirectional Encoder to Autoregressive Decoder for Sequential Recommendation

[7] Dual Unbiased Recommender Learning for Implicit Feedback

[8] Info-flow Enhanced GANs for Recommender

[9] Augmenting Sequential Recommendation with Pseudo-Prior Items via Reversely Pre-training Transformer

https://arxiv.org/abs/2105.00522

https://github.com/DyGRec/ASReP

[10] Neural Representations in Hybrid Recommender Systems: Prediction versus Regularization

https://arxiv.org/abs/2010.06070

[11] Cluster-Based Bandits: Fast Cold-Start for Recommender System New Users

https://www.scss.tcd.ie/Doug.Leith/pubs/sigir16.pdf

[12] Social Recommendation with Implicit Social Influence

[13] Underestimation Refinement: A General Enhancement Strategy for Exploration in Recommendation Systems

[14] Counterfactual Explanations for Neural Recommenders

https://arxiv.org/abs/2105.05008

https://github.com/hieptk/accent

[15] Sequential Recommendation for Cold-start Users with Meta Transitional Learning

http://people.tamu.edu/~jwang713/pubs/MetaTL-sigir2021

[16] Cross-Batch Negative Sampling for Training Two-Tower Recommenders

[17] Empowering News Recommendation with Pre-trained Language Models

https://arxiv.org/abs/2104.07413

[18] Bayesian Critiquing with Keyphrase Activation Vectors for VAE-based Recommender Systems

https://ssanner.github.io/papers/sigir21_tcavcrit.pdf

[19] ConsisRec: Enhancing GNN for Social Recommendation via Consistent Neighbor Aggregation

https://arxiv.org/abs/2105.02254

https://github.com/YangLiangwei/ConsisRec

[20] ICAI-SR: Item Categorical Attribute Integrated Sequential Recommendation

[21] AMM: Attentive Multi-field Matching for News Recommendation

[22] Temporal Augmented Graph Neural Networks for Session-Based Recommendations

[23] Transfer-Meta Framework for Cross-domain Recommendation to Cold-Start Users

https://arxiv.org/abs/2105.0478

---

参考的Github仓库如下，欢迎支持与star。

1. https://github.com/hongleizhang/RSPapers

2. https://github.com/jihoo-kim/RecSys-Papers-from-SIGIR-2021

推荐阅读

• KDD2021推荐系统论文集锦
  

• WSDM2021推荐系统论文集锦（附下载）

• AAAI2021推荐系统论文清单
  

• 围观RecSys2020 | 推荐系统顶会说了啥？
  

• CIKM2020最新9篇推荐系统相关论文
  

• KDD2020推荐系统论文聚焦
  

• (ACL+ICML)2020推荐系统相关论文聚焦
  

• SIGIR2020推荐系统论文聚焦

• WSDM2020推荐系统论文打包下载

• IJCAI'20最新推荐系统论文聚焦
  

• ECAI2020推荐系统论文聚焦
  

• WWW2020推荐系统论文合集
  

• AAAI2020推荐系统论文集锦

• IJCAI'19最新推荐系统论文分享

喜欢的话点个在看吧????