  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.
