Making Recommender Systems Forget: Learning and Unlearning for Erasable Recommendation

TL;DR

This paper introduces LASER, a novel framework for machine unlearning in recommender systems that effectively unlearns user data while maintaining high recommendation quality, addressing privacy compliance challenges.

Contribution

LASER is the first general framework for unlearning in recommendation systems that leverages collaborative filtering and curriculum learning to unlearn efficiently.

Findings

LASER achieves faster unlearning compared to existing methods.

LASER outperforms state-of-the-art in recommendation utility after unlearning.

Theoretical analysis confirms LASER's effectiveness and efficiency.

Abstract

Privacy laws and regulations enforce data-driven systems, e.g., recommender systems, to erase the data that concern individuals. As machine learning models potentially memorize the training data, data erasure should also unlearn the data lineage in models, which raises increasing interest in the problem of Machine Unlearning (MU). However, existing MU methods cannot be directly applied into recommendation. The basic idea of most recommender systems is collaborative filtering, but existing MU methods ignore the collaborative information across users and items. In this paper, we propose a general erasable recommendation framework, namely LASER, which consists of Group module and SeqTrain module. Firstly, Group module partitions users into balanced groups based on their similarity of collaborative embedding learned via hypergraph. Then SeqTrain module trains the model sequentially on all groups with curriculum learning. Both theoretical analysis and experiments on two real-world datasets demonstrate that LASER can not only achieve efficient unlearning, but also outperform the state-of-the-art unlearning framework in terms of model utility.