- RecSim: A Configurable Recommender Systems Simulation Platform // Eugene Ie (Google Research); Chih-wei Hsu ( Google Research); Martin Mladenov (Google); Sanmit Narvekar (University of Texas at Austin); Jing Wang (Google Research); Rui Wu (Google Research); Vihan Jain (Google Research); Craig Boutilier (Google Research)
- AutoML for Contextual Bandit PayOff // Praneet Dutta (Google); Man Kit Cheuk (Google); Jonathan Kim (Google); Massimo Mascaro (Google)
- On the Value of Bandit Feedback for Offline Recommender System Evaluation // Olivier Jeunen (University of Antwerp); David J Rohde (CRITEO); Flavian Vasile (Criteo)
- Debiased Offline Evaluation of Recommender Systems: A Weighted-Sampling Approach // Diego Carraro (Insight Centre for Data Analytics); Derek Bridge (Insight Centre for Data Analytics)
- Ubiquitous Recommender System with Models of User Awareness // Takashi Imai (Fujitsu Laboratories LTD.)*; Takashi Ohno (Fujitsu Laboratories LTD.); Riichirou Take (Fujitsu Laboratories LTD.)
- All You Need is Ratings: A Clustering Approach to Synthetic Rating Datasets Generation // Diego Monti (Politecnico di Torino); Giuseppe Rizzo (LINKS Foundation); Maurizio Morisio (Politecnico di Torino)
- Machine Learning is Natural Experiment // Yusuke Narita (Yale University); Kohei Yata (Yale University)
- Reinforcement Learning Meets Double Machine Learning // Yusuke Narita (Yale University); Shota Yasui (CyberAgent Inc.); Kohei Yata (Yale University)
- How robust is MovieLens? A dataset analysis for recommender systems // Anne-Marie Tousch (Criteo AI Lab)
- Ranking metrics on non-shuffled traffic // Alexandre Gilotte (Criteo)
- Learning from Bandit Feedback: An Overview of the State-of-the-art // Olivier Jeunen (University of Antwerp); Dmytro Mykhaylov (Criteo); David J Rohde (CRITEO); Flavian Vasile (Criteo); Alexandre Gilotte (Criteo); Martin Bompaire (Criteo)
- Counterfactual Cross Validation // Yuta Saito (Tokyo Institute of Technology); Shota Yasui (Cyberagent)
- Evaluating Tag Recommendations for E-Book Annotation Using a Semantic Similarity Metric // Emanuel Lacic (Know-Center GmbH); Dominik Kowald (Know-Center GmbH); Dieter Theiler (Know-Center GmbH); Matthias Traub (Know-Center GmbH); Lucky Kuffer (HGV GmbH); Stefanie Lindstaedt (Know-Center GmbH), Elisabeth Lex (TU Graz)
- Using the Open Meta Kaggle Dataset to Evaluate Tripartite Recommendations in Data Markets // Dominik Kowald (Know-Center GmbH); Matthias Traub (Know-Center GmbH); Dieter Theiler (Know-Center GmbH); Heimo Gursch (Know-Center GmbH); Emanuel Lacic (Know-Center GmbH); Stefanie Lindstaedt (Know-Center GmbH); Roman Kern (Graz University of Technology); Elisabeth Lex (TU Graz)
- Marginal Posterior Sampling for Slate Bandits // Maria Dimakopoulou (Netflix); Nikos Vlassis (Netflix); Yves Raimond (Netflix); Tony Jebara (Netflix)
- How Sensitive is Recommendation Systems' Offline Evaluation to Popularity? // Amir H Jadidinejad (University of Glasgow); Craig Macdonald (University of Glasgow); Iadh Ounis (University of Glasgow)
- Recap: Designing a more Efficient Estimator for Off-policy Evaluation in Bandits with Large Action Spaces // Ajinkya More (Netflix), Linas Baltrunas (Netflix), Nikos Vlassis (Netflix), Justin Basilico (Netflix)
- Recommendation System-based Upper Confidence Bound for Online Advertising // Nhan Nguyen-Thanh (Universite Paris-Sud); Dana Marinca (University of Versailles); Kinda Khawam (University of Versailles); David J Rohde (CRITEO); Flavian Vasile (Criteo); Elena Simona Lohan (Tampere University of Technology); Steven Martin (University of Paris-Sud); Dominique Quadri (University of Paris-Sud)
- Sudden Death: A New Way to Compare Recommendation Diversification // Derek Bridge (University College Cork); Mesut Kaya (University College Cork); Pablo Castells (Universidad Autónoma de Madrid)
- Generalizable Recommendation to a Target Population by Leveraging Randomized and Observational Studies // Lili Wu (North Carolina State University); Shu Yang (North Carolina State University)
- Sequence-aware Reinforcement Learning over Knowledge Graphs // Rishabh Mehrotra (Spotify); Ashish Gupta (WalmartLabs)
- Learning to unlearn biased feedback loops in recommendation systems // Hang Wu (Georgia Institute of Technology)
- Towards Sharing Task Environments to Support Reproducible Evaluations of Interactive Recommender Systems // Andrea P Barraza (Insight Centre for Data Analytics); Mathieu d’Aquin (Insight Centre for Data Analytics at National University of Ireland Galway)
