Accepted Contributions

List of all long papers accepted for RecSys 2020 (in alphabetical order).

  • A Method to Anonymize Business Metrics to Publishing Implicit Feedback Datasets
    Yoshifumi Seki, Takanori Maehara
  • A Ranking Optimization Approach to Latent Linear Critiquing in Conversational Recommender System
    Hanze Li, Scott Sanner, Kai Luo, Ga Wu
  • Cascading Hybrid Bandits: Online Learning to Rank for Relevance and Diversity
    Chang Li, Haoyun Feng, Maarten de Rijke
  • Content-Collaborative Disentanglement Representation Learning for Enhanced Recommendation
    Yin Zhang, Ziwei Zhu, Yun He, James Caverlee
  • Contextual and Sequential User Embeddings for Large-Scale Music Recommendation
    Casper Hansen, Christian Hansen, Lucas Maystre, Rishabh Mehrotra, Brian Brost, Federico Tomasi, Mounia Lalmas
  • Contextual User Browsing Bandits for Large-Scale Online Mobile Recommendation
    Xu HE, Bo An, Yanghua Li, Haikai Chen, Qingyu Guo, Xin Li, Zhirong Wang
  • Debiasing Item-to-Item Recommendations With Small Annotated Datasets
    Tobias Schnabel, Paul N. Bennett
  • Deconstructing the Filter Bubble: User Decision-Making and Recommender Systems
    Guy Aridor, Duarte Goncalves, Shan Sikdar
  • Doubly Robust Estimator for Ranking Metrics with Post-Click Conversions
    Yuta Saito
  • Ensuring Fairness in Group Recommendations by Rank-Sensitive Balancing of Relevance
    Mesut Kaya, Derek Bridge, Nava Tintarev
  • Exploiting Performance Estimates for Augmenting Recommendation Ensembles
    Gustavo Penha, Rodrygo L. T. Santos
  • Exploring Clustering of Bandits for Online Recommendation System
    Liu Yang, Bo Liu, Leyu Lin, Feng Xia, Kai Chen, Qiang Yang
  • FISSA: Fusing Item Similarity Models with Self-Attention Networks for Sequential Recommendation
    Jing Lin, Weike Pan, Zhong Ming
  • From the Lab to Production: A Case Study of Session-Based Recommendations in the Home-Improvement Domain
    Pigi Kouki, Ilias Fountalis, Nikolaos Vasiloglou, Xiquan Cui, Edo Liberty, Khalifeh Al Jadda
  • Global and Local Differential Privacy for Collaborative Bandits
    Huazheng Wang, Qian Zhao, Qingyun Wu, Shubham Chopra, Abhinav Khaitan, Hongning Wang
  • Goal-driven Command Recommendations for Analysts
    Samarth Aggarwal, Rohin Garg, Abhilasha Sancheti, Bhanu Prakash Reddy Guda, Iftikhar Ahamath Burhanuddin
  • ImRec: Learning Reciprocal Preferences Using Images
    James Neve, Ryan McConville
  • In-Store Augmented Reality-Enabled Product Comparison and Recommendation
    Jesús Omar Álvarez Márquez, Jürgen Ziegler
  • Keeping Dataset Biases out of the Simulation: A Debiased Simulator for Reinforcement Learning based Recommender Systems
    Jin Huang, Harrie Oosterhuis, Maarten de Rijke, Herke van Hoof
  • KRED: Knowledge-Aware Document Representation for News Recommendations
    Danyang Liu, Jianxun Lian, Shiyin Wang, Ying Qiao, Jiun-Hung Chen, Guangzhong Sun, Xing Xie
  • Learning to Collaborate in Multi-Module Recommendation via Multi-Agent Reinforcement Learning without Communication
    Xu HE, Bo An, Yanghua Li, Haikai Chen, Rundong Wang, Xinrun Wang, Runsheng Yu, Xin Li, Zhirong Wang
  • Making Neural Networks Interpretable with Attribution: Application to Implicit Signals Prediction
    Darius Afchar, Romain Hennequin
  • MultiRec: A Multi-Relational Approach for Unique Item Recommendation in Auction Systems
    Ahmed Rashed, Shayan Jawed, Lars Schmidt-Thieme, Andre Hintsches
  • Offline Contextual Multi-armed Bandits for Mobile Health Interventions: A Case Study on Emotion Regulation
    Mawulolo Koku Ameko, Miranda L. Beltzer, Lihua Cai, Mehdi Boukhechba, Bethany Teachman, Laura E Barnes
  • On Target Item Sampling in Offline Recommender System Evaluation
    Rocío Cañamares, Pablo Castells
  • Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations
    Hongyan Tang, Junning Liu, Ming Zhao, Xudong Gong
  • PURS: Personalized Unexpected Recommender System for Improving User Satisfaction
    Pan Li, Maofei Que, Zhichao Jiang, YAO HU, Alexander Tuzhilin
  • Recommendations as Graph Explorations
    Marialena Kyriakidi, Georgia Koutrika, Yannis Ioannidis
  • Recommending the Video to Watch Next: An Offline and Online Evaluation at YOUTV.de
    Panagiotis Symeonidis, Andrea Janes, Dmitry Chaltsev, Philip Giuliani, Daniel Morandini, Andreas Unterhuber, Ludovik Coba, Markus Zanker
  • RecSeats: A Hybrid Convolutional Neural Network Choice Model for Seat Recommendations at Reserved Seating Venues
    Théo Moins, Daniel Aloise, Simon J. Blanchard
  • Revisiting Adversarially Learned Injection Attacks Against Recommender Systems
    Jiaxi Tang, Hongyi Wen, Ke Wang
  • SSE-PT: Sequential Recommendation Via Personalized Transformer
    Liwei Wu, Shuqing Li, Cho-jui Hsieh, James Sharpnack
  • TAFA: Two-headed Attention Fused Autoencoder for Context-Aware Recommendations
    Jin Peng Zhou, Zhaoyue Cheng, Felipe Perez, Maksims Volkovs
  • Theoretical Modeling of the Iterative Properties of User Discovery in a Collaborative Filtering Recommender System
    Sami Khenissi, Boujelbene Mariem, Olfa Nasraoui
  • Towards Safety and Sustainability: Designing Local Recommendations for Post-pandemic World
    Gourab K Patro, Abhijnan Chakraborty, Ashmi Banerjee, Niloy Ganguly
  • Unbiased Ad Click Prediction for Position-aware Advertising Systems
    Bowen Yuan, Yaxu Liu, Jui-Yang Hsia, Zhenhua Dong, Chih-Jen Lin
  • Unbiased Learning for the Causal Effect of Recommendation
    Masahiro Sato, Sho Takemori, Janmajay Singh, Tomoko Ohkuma
  • What does BERT know about books, movies and music? Probing BERT for Conversational Recommendation
    Gustavo Penha, Claudia Hauff
  • Who Doesn’t Like Dinosaurs? Finding and Eliciting Richer Preferences for Recommendation
    Tobias Schnabel, Gonzalo Ramos, Saleema Amershi
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