Netflix and Forget: Fast Severance From Memorizing Training Data in Recommendations

Mimee Xu, Jiankai Sun, Xin Yang, Yuanshun Yao, Chong Wang

Abstract:

Suppose a person, who has streamed rom-coms exclusively with their significant
other, suddenly breaks up.
Consider an expecting mom, who has shopped for baby clothes, miscarries.
Their streaming and shopping recommendations, however, do not necessarily update, serving as unhappy reminders of their loss.
One approach is to implement the Right To Be Forgotten for recommendation systems built from user data, with the goal of updating downstream recommendations to reflect the removal without incurring the cost of re-training.
Inspired by solutions to the original Netflix challenge~\citep{koren2009bellkor}, we develop Unlearn-ALS, which is more aggressively forgetful of select data than fine-tuning. In theory, it is consistent with retraining without model degradation. Empirically, it shows fast convergence, and can be applied directly to any bi-linear models regardless of the training procedure.