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

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