Machine unlearning in recommendation systems works better when you target the specific model components most affected by deleted data rather than applying uniform updates across the entire model.
This paper addresses the challenge of removing user data from multimodal recommendation systems efficiently. The authors show that existing unlearning methods apply uniform updates across the entire model, but deleted-data influence is actually concentrated in specific areas like ranking behavior and certain network layers.