RobustRecSys 2024
Design, Evaluation, and Deployment of Robust Recommender Systems 2024

Table of Contents

• Summary: There were 9 papers submitted for peer-review to this workshop. Out of these, 8 papers were accepted for this volume, 1 as a regular paper and 7 as short papers.

• The Role of Fake Users in Sequential Recommender Systems 1-7
  Filippo Bettello
• Removing Bad Influence: Identifying and Pruning Detrimental Users in Collaborative Filtering Recommender Systems 8-11
  Philipp Meister, Lukas Wegmeth, Tobias Vente, Joeran Beel
• Greedy Ensemble Selection for Top-N Recommendations 12-16
  Tobias Vente, Zainil Mehta, Lukas Wegmeth, Joeran Beel
• Reducing Popularity Influence by Addressing Position Bias 17-23
  Andrii Dzhoha, Alexey Kurennoy, Vladimir Vlasov, Marjan Celikik
• Addressing bias in Recommender Systems: A Case Study on Data Debiasing Techniques in Mobile Games 24-29
  Yixiong Wang, Maria Paskevich, Hui Wang
• Robust Training Objectives Improve Embedding-based Retrieval in Industrial Recommendation Systems 30-34
  Matthew Kolodner, Mingxuan Ju, Zihao Fan, Tong Zhao, Elham Ghazizadeh, Yan Wu, Neil Shah, Yozen Liu
• Robust Solutions for Ranking Variability in Recommender Systems 35-40
  Bonifacio Marco Francomano, Federico Siciliano, Fabrizio Silvestri
• Robust Training of Sequential Recommender Systems with Missing Input Data 41-50
  Federico Siciliano, Shoval Lagziel, Iftah Gamzu, Gabriele Tolomei