Programme

October 14th 2024 - Room A

09:00 - 09:10 Welcome by the organisers

09:10 – 10:00 Tutorial: Yuta Saito               Counterfactual Learning and Evaluation for Recommender Systems:
          from Foundations to Emerging Challenges in the Era of LLMs

10:00 – 10:30 Paper Session #1

• AutoOPE: Automated Off-Policy Estimator Selection
  Nicolò Felicioni, Michael Benigni and Maurizio Ferrari Dacrema.

• A Simple Model to Estimate Sharing Effects in Social Networks
  Olivier Jeunen.

10:30 – 11:15 Coffee Break

11:15 – 12:15 Keynote: Ciarán Gilligan-Lee Counterfactual Reasoning and What it’s Good for

12:15 – 12:45 Paper Session #2

• Combining Open-box Simulation and Importance Sampling for Tuning Large-Scale Recommenders
  Kaushal Paneri, Michael J. Munje, Kailash Singh Maurya, Adith Swaminathan and Yifan Shi.

• Causal Discovery in Recommender Systems: Example and Discussion
  Emanuele Cavenaghi, Fabio Stella and Markus Zanker.

12:45 – 14:30 Lunch Break

14:30 – 15:30 Keynote: Thorsten Joachims Towards Steerable AI Systems

15:30 – 16:00 Paper Session #3

• Logarithmic Smoothing for Pessimistic Off-Policy Evaluation, Selection and Learning
  Otmane Sakhi, Imad Aouali, Pierre Alquier and Nicolas Chopin.

• Proximal Ranking Policy Optimization for Practical Safety in Counterfactual Learning to Rank
  Shashank Gupta, Harrie Oosterhuis and Maarten de Rijke.

16:00 – 16:45 Coffee Break

16:45 – 17:15 Paper Session #4

• The Relevance of Item-Co-Exposure For Exposure Bias Mitigation
  Thorsten Krause, Alina Deriyeva, Jan Heinrich Beinke, Gerrit Bartels and Oliver Thomas.

• Causal Feature Selection Method for Contextual Multi-Armed Bandits in Recommender System
  Zhenyu Zhao and Yexi Jiang.

17:15 – 18:15 Poster Session [located in the Atrio Cherubini]

All times in local time zone of Bari, Italy, (CEST, UTC+2).