Contributions

REVEAL '22

  • A Contextual Bandit Problem with a Bounded (O(1)) Regret Policy [PDF]
    Hyunwook Kang (Texas A&M University)*; P. R. Kumar (Texas A&M University)

  • Causal Adaptive Learning for Recommendations [PDF]
    Maria Dimakopoulou (Spotify)*

  • Control Variate Diagnostics for Detecting Problems in Logged Bandit Feedback [PDF]
    Ben London (Amazon)*; Thorsten Joachims (Cornell)

  • Extending Open Bandit Pipeline to Simulate Industry Challenges [PDF]
    Bram van den Akker (Booking.com)*; Niklas Weber (booking.com); Felipe Moraes (Booking.com); Dmitri Goldenberg (Booking.com)

  • Modelling User Preferences using a Partially Observed Markov Decision Problem for a Reinforcement Learning Sequence-Aware Recommender [PDF]
    Aayush S Roy (UCD Dublin)*; Aonghus Lawlor (UCD); Neil Joseph Hurley (University College Dublin)

  • OFRL: Designing an Offline Reinforcement Learning and Policy Evaluation Platform from Practical Perspectives [PDF]
    Haruka Kiyohara (Tokyo Institute of Technology)*; Kosuke Kawakami (negocia, Inc.)

  • Safe and Deployment Efficient Policy Learning for Exploring Novel Actions in Recommender Systems [PDF]
    Haruka Kiyohara (Tokyo Institute of Technology)*; Yusuke Narita (Yale University); Kei Tateno (Sony Group Corporation); Takuma Udagawa (Sony Group Corporation)

  • Sales Channel Optimization via Simulations based on Observational Data with Delayed Rewards: A Case Study at LinkedIn [PDF]
    Diana Negoescu (LinkedIn Corporation)*; Pasha Khosravi (University of California Los Angeles); Shadow Zhao (LinkedIn Corporation); Nanyu Chen (Gopuff); Parvez Ahammad (LinkedIn); Humberto Gonzalez (LinkedIn Corporation)

  • SkipAwareRec: A Sequential and Interactive Music Recommendation System [PDF]
    Rui Ramos (INESC TEC); João Vinagre (LIAAD - INESC TEC)*

  • When to Target Customers? Retention Management using Dynamic Off-Policy Policy Learning [PDF]
    Kosuke Uetake (Yale School of Management)*; Kohei Yata (Yale University); Ryosuke Okada (ZOZO Inc.); Ryuya Ko (University of Tokyo)


CONSEQUENCES '22

  • Adaptive Experimental Design and Counterfactual Inference [PDF]
    Tanner Fiez (Amazon)*; Lalit Jain (University of Washington); Houssam Nassif (amazon); Sergio Gamez (Amazon); Arick Chen (Amazon)

  • Are Neural Click Models Pointwise IPS Rankers? [PDF]
    Philipp K Hager (University of Amsterdam)*; Maarten de Rijke (University of Amsterdam); Onno Zoeter (Booking)

  • Causal Evaluation of Item Fairness in Impression Delivery [PDF]
    Winston Chou (Netflix)*; Nathan Kallus (Cornell University)

  • CLEAR: Causal Explanations from Attention in Neural Recommenders [PDF]
    Shami Nisimov (Intel Labs); Raanan Y. Rohekar (Intel Labs)*; Yaniv Gurwicz (Intel Labs); Guy Koren (Intel Labs); Gal Novik (Intel Labs)

  • Improving Accuracy of Off-Policy Evaluation via Policy Adaptive Estimator Selection [PDF]
    Takuma Udagawa (Sony Group Corporation)*; Haruka Kiyohara (Tokyo Institute of Technology); Yusuke Narita (Yale University); Kei Tateno (Sony Group Corporation)

  • Leveraging Context-dependent Click Model for Off-Policy Evaluation of Ranking Policies [PDF]
    Haruka Kiyohara (Tokyo Institute of Technology)*; Nobuyuki Shimizu (Yahoo Japan Corporation); Yasuo Yamamoto (Yahoo! Japan)

  • Off-policy evaluation for learning-to-rank via interpolating the item-position model and the position-based model [PDF]
    Alexander Buchholz (Amazon)*; Ben London (Amazon); Giuseppe Di Benedetto (Amazon); Thorsten Joachims (Cornell)

  • Significant heterogeneous double machine learning for recommendation [PDF]
    John S Moreland (Amazon)*; Zuqi Shang (Amazon)

  • The Bandwagon Effect: Not Just Another Bias [PDF]
    Norman Knyazev (Radboud University)*; Harrie Oosterhuis (Radboud University)

  • VAE-IPS: A Deep Generative Recommendation Method for Unbiased Learning From Implicit Feedback [PDF]
    Shashank Gupta (University of Amsterdam)*; Harrie Oosterhuis (Radboud University); Maarten de Rijke (University of Amsterdam)