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)