2 papers
cs.IR2026
ERASE -- A Real-World Aligned Benchmark for Unlearning in Recommender Systems
Pierre Lubitzsch, Maarten de Rijke, Sebastian Schelter
Machine unlearning (MU) enables the removal of selected training data from trained models, to address privacy compliance, security, and liability issues in recommender systems. Exi…
cs.IR2025
Towards a Real-World Aligned Benchmark for Unlearning in Recommender Systems
Pierre Lubitzsch, Olga Ovcharenko, Hao Chen +2
Modern recommender systems heavily leverage user interaction data to deliver personalized experiences. However, relying on personal data presents challenges in adhering to privacy…