RecSys2021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15届推荐系统年会（ACM RecSys 2021）将于9月27日-10月1日在荷兰阿姆斯特丹举行，大会表明可以以更包容的方式通过线上的形式允许有需要的人参与其中。去年的推荐系统年会论文集锦请参考：围观RecSys2020 | 推荐系统顶会说了啥？。

需要说明的是，本年度的会议论文接收列表（The List of Accepted Papers）已于2021年7月8日在官方网站公布，其中包括49篇常规论文（Regular Papers），3篇复现性论文（Reproducibility Papers），23篇最新成果论文（Late-breaking Results Papers），10篇演示论文（Demo Papers），8篇博士研讨会论文（Doctoral Seminar Papers），14篇工业界演讲（Industry Talks）以及11篇海报（Posters）。官网地址：

https://recsys.acm.org/recsys21/accepted-contributions/

通过对本次年会论文以及教程的总结发现，此次大会主要聚焦在了推荐系统中的Bias问题、冷启动问题、对话推荐系统、推荐中的隐私和安全问题、多模态推荐系统、推荐系统的可解释性以及会话推荐等。

大会教程为以下6个：

• Counterfactual Learning and Evaluation for Recommender Systems: Foundations, Implementations, and Recent Advances by Yuta Saito (Cornell University, USA) and Thorsten Joachims (Cornell University, USA)
• Multi-Modal Recommender Systems: Hands-On Exploration by Quoc-Tuan Truong (Singapore Management University, Singapore), Aghiles Salah (Rakuten Institute of Technology, France), and Hady W. Lauw (Singapore Management University, Singapore)
• End-to-End Session-Based Recommendation on GPU by Gabriel de Souza Pereira Moreira (NVIDIA, Brazil), Sara Rabhi (NVIDIA, Canada), Ronay Ak (NVIDIA, USA), and Benedikt Schifferer (NVIDIA, USA)
• Pursuing Privacy in Recommender Systems: the View of Users and Researchers from Regulations to Applications by Vito Walter Anelli (Polytechnic University of Bari, Italy), Luca Belli (Twitter, USA), Yashar Deldjoo, Tommaso Di Noia, Antonio Ferrara, Fedelucio Narducci, and Claudio Pomo (Polytechnic University of Bari, Italy)
• Conversational Recommendation: Formulation, Methods, and Evaluation by Wenqiang Lei (National University of Singapore, Singapore), Chongming Gao (University of Science and Technology of China, China), and Maarten de Rijke (University of Amsterdam & Ahold Delhaize, Netherlands)
• Bias Issues and Solutions in Recommender System by Jiawei Chen (University of Science and Technology of China, China), Xiang Wang (National University of Singapore, Singapore), Fuli Feng (National University of Singapore, Singapore), and Xiangnan He (University of Science and Technology of China, China)

另外，大会还提供了可复现性的赛道，有3篇论文在此行列，分别涉及到序列推荐中的采样策略、对话推荐系统以及重温NCF与MF，具体的论文名称以及作者如下：

• A Case Study on Sampling Strategies for Evaluating Neural Sequential Item Recommendation Models by Alexander Dallmann, Daniel Zoller, Andreas Hotho (Data Science Chair, University of Würzburg, Würzburg, Germany)
• Generation-based vs. Retrieval-based Conversational Recommendation: A User-Centric Comparison by Ahtsham Manzoor and Dietmar Jannach (University of Klagenfurt, Klagenfurt, Austria)
• Reenvisioning the comparison between Neural Collaborative Filtering and Matrix Factorization by Vito Walter Anelli (Polytechnic University of Bari, Bari, Italy), Alejandro Bellogin (Information Retrieval Group, Universidad Autonoma de Madrid, Madrid, Spain), Tommaso Di Noia Polytechnic (University of Bari, Bari, Italy), and Claudio Pomo (Polytechnic University of Bari, Bari, Italy)

最后，小编为大家收集整理了该年会的论文列表，大家可以对自己感兴趣或者自己研究方向的论文进行更深入的阅读。其中对论文进行总结发现，除了以上列出的大类外，还有一些前沿的研究技术，比如涉及到的强化学习、联邦学习等。

• A Payload Optimization Method for Federated Recommender Systems Farwa K. Khan, Adrian Flanagan, Kuan Eeik Tan, Zareen Alamgir, and Muhammad Ammad-ud-din
• Accordion: A Trainable Simulator for Long-Term Interactive Systems James McInerney, Ehtsham Elahi, Justin Basilico, Yves Raimond, and Tony Jebara
• An Audit of Misinformation Filter Bubbles on YouTube: Bubble Bursting and Recent Behavior Changes Matus Tomlein, Branislav Pecher, Jakub Simko, Ivan Srba, Robert Moro, Elena Stefancova, Michal Kompan, Andrea Hrckova, Juraj Podrouzek, and Maria Bielikova
• Black-Box Attacks on Sequential Recommenders via Data-Free Model Extraction Zhenrui Yue, Zhankui He, Huimin Zeng, and Julian McAuley
• Burst-induced Multi-Armed Bandit for Learning Recommendation Rodrigo Alves, Antoine Ledent, and Marius Kloft
• cDLRM: Look Ahead Caching for Scalable Training of Recommendation Models Keshav Balasubramanian, Abdulla Alshabanah, Joshua D Choe, and Murali Annavaram
• Cold Start Similar Artists Ranking with Gravity-Inspired Graph Autoencoders Guillaume Salha-Galvan, Romain Hennequin, Benjamin Chapus, Viet-Anh Tran, and Michalis Vazirgiannis
• Debiased Explainable Pairwise Ranking from Implicit Feedback Khalil Damak, Sami Khenissi, and Olfa Nasraoui
• Denoising User-aware Memory Network for Recommendation Zhi Bian, Shaojun Zhou, Hao Fu, Qihong Yang, Zhenqi Sun, Junjie Tang, Guiquan Liu, kaikui liu, and Xiaolong Li
• Designing Online Advertisements via Bandit and Reinforcement Learning Yusuke Narita, Shota Yasui, and Kohei Yata
• Evaluating Off-Policy Evaluation: Sensitivity and Robustness Yuta Saito, Takuma Udagawa, Haruka Kiyohara, Kazuki Mogi, Yusuke Narita, and Kei Tateno
• EX3: Explainable Attribute-aware Item-set Recommendations Yikun Xian, Tong Zhao, Jin Li, Jim Chan, Andrey Kan, Jun Ma, Xin Luna Dong, Christos Faloutsos, George Karypis, S. Muthukrishnan, and Yongfeng Zhang
• Fast Multi-Step Critiquing for VAE-based Recommender Systems Diego Antognini and Boi Faltings
• Follow the guides: disentangling human and algorithmic curation in online music consumption Quentin Villermet, Jérémie Poiroux, Manuel Moussallam, Thomas Louail, and Camille Roth
• Hierarchical Latent Relation Modeling for Collaborative Metric Learning Viet-Anh Tran, Guillaume Salha-Galvan, Romain Hennequin, and Manuel Moussallam
• I want to break free! Recommending friends from outside the echo chamber Antonela Tommasel, Juan Manuel Rodriguez, and Daniela Godoy
• Information Interactions in Outcome Prediction: Quantification and Interpretation using Stochastic Block Models Gaël Poux-Médard, Julien Velcin, and Sabine Loudcher
• Large-scale Interactive Conversational Recommendation System Ali Montazeralghaem, James Allan, and Philip S. Thomas
• Large-Scale Modeling of Mobile User Click Behaviors Using Deep Learning Xin Zhou and Yang Li
• Learning An Adaptive Meta Model-Generator for Incrementally Updating Recommender Systems Danni Peng, Sinno Jialin Pan, Jie Zhang, and Anxiang Zeng
• Learning to Represent Human Motives for Goal-directed Web Browsing Jyun-Yu Jiang, Chia-Jung Lee, Longqi Yang, Bahareh Sarrafzadeh, Brent Hecht, Jaime Teevan
• Local Factor Models for Large-Scale Inductive Recommendation Longqi Yang, Tobias Schnabel, Paul N. Bennett, and Susan Dumais
• Matrix Factorization for Collaborative Filtering Is Just Solving an Adjoint Latent Dirichlet Allocation Model After All Florian Wilhelm
• Mitigating Confounding Bias in Recommendation via Information Bottleneck Dugang Liu, Pengxiang Cheng, Hong Zhu, Zhenhua Dong, Xiuqiang He, Weike Pan, and Zhong Ming
• Negative Interactions for Improved Collaborative-Filtering: Don’t go Deeper, go Higher Harald Steck and Dawen Liang
• Next-item Recommendations in Short Sessions Wenzhuo Song, Shoujin Wang, Yan Wang, and SHENGSHENG WANG
• Online Evaluation Methods for the Causal Effect of Recommendations Masahiro Sato
• Page-level Optimization of e-Commerce Item Recommendations Chieh Lo, Hongliang Yu, Xin Yin, Krutika Shetty, Changchen He, Kathy Hu, Justin M Platz, Adam Ilardi, and Sriganesh Madhvanath
• Partially Observable Reinforcement Learning for Dialog-based Interactive Recommendation Yaxiong Wu, Craig Macdonald, and Iadh Ounis,
• Pessimistic Reward Models for Off-Policy Learning in Recommendation Olivier Jeunen and Bart Goethals
• Privacy Preserving Collaborative Filtering by Distributed Mediation Alon Ben Horin, and Tamir Tassa
• ProtoCF: Prototypical Collaborative Filtering for Few-shot Item Recommendation Aravind Sankar, Junting Wang, Adit Krishnan, and Hari Sundaram
• Recommendation on Live-Streaming Platforms: Dynamic Availability and Repeat Consumption Jeremie Rappaz, Julian McAuley, and Karl Aberer
• Reverse Maximum Inner Product Search: How to efficiently find users who would like to buy my item? Daichi Amagata and Takahiro Hara
• Semi-Supervised Visual Representation Learning for Fashion Compatibility Ambareesh Revanur, Vijay Kumar, and Deepthi Sharma
• “Serving Each User”: Supporting Different Eating Goals Through a Multi-List Recommender Interface Alain Starke, Edis Asotic, and Christoph Trattner
• Shared Neural Item Representations for Completely Cold Start Problem Ramin Raziperchikolaei, Guannan Liang, and Young-joo Chung
• Sparse Feature Factorization for Recommender Systems with Knowledge Graphs Antonio Ferrara, Vito Walter Anelli, Tommaso Di Noia, and Alberto Carlo Maria Mancino
• Stronger Privacy for Federated Collaborative Filtering With Implicit Feedback Lorenzo Minto, Moritz Haller, Ben Livshits, and Hamed Haddadi
• The Dual Echo Chamber: Modeling Social Media Polarization for Interventional Recommending Tim Donkers and Jürgen Ziegler
• The role of preference consistency, defaults and musical expertise in users’ exploration behavior in a genre exploration recommender Yu Liang and Martijn C. Willemsen
• Together is Better: Hybrid Recommendations Combining Graph Embeddings and Contextualized Word Representations Marco Polignano, Cataldo Musto, Marco de Gemmis, Pasquale Lops, and Giovanni Semeraro
• Top-K Contextual Bandits with Equity of Exposure Olivier Jeunen and Bart Goethals
• Tops, Bottoms, and Shoes: Building Capsule Wardrobes via Cross-Attention Tensor Network Huiyuan Chen, Yusan Lin, Fei Wang, and Hao Yang
• Towards Source-Aligned Variational Models for Cross-Domain Recommendation Aghiles Salah, Thanh Binh Tran, and Hady Lauw
• Towards Unified Metrics for Accuracy and Diversity for Recommender Systems Javier Parapar and Filip Radlinski
• Transformers4Rec: Bridging the Gap between NLP and Sequential / Session-Based Recommendation Gabriel de Souza Pereira Moreira, Sara Rabhi, Jeong Min Lee, Ronay Ak, and Even Oldridge
• User Bias and Unfairness of Recommendation Algorithms in Beyond-Accuracy Measurements Ningxia Wang, and Li Chen
• Values of Exploration in Recommender Systems Minmin Chen, Yuyan Wang, Can Xu, Ya Le, mohit sharma, Lee Richardson, and Ed Chi