Accepted Contributions

All papers can be found in the ACM Digital Library.

List of all papers accepted for RecSys 2021 (in alphabetical order).


  • 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 in Beyond-Accuracy Measurement of Recommendation Algorithms
    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
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