  Recent regulations on the Right to be Forgotten have greatly influenced the
way of running a recommender system, because users now have the right to
withdraw their private data. Besides simply deleting the target data in the
database, unlearning the associated data lineage e.g., the learned personal
features and preferences in the model, is also necessary for data withdrawal.
Existing unlearning methods are mainly devised for generalized machine learning
models in classification tasks. In this paper, we first identify two main
disadvantages of directly applying existing unlearning methods in the context
of recommendation, i.e., (i) unsatisfactory efficiency for large-scale
recommendation models and (ii) destruction of collaboration across users and
items. To tackle the above issues, we propose an extra-efficient recommendation
unlearning method based on Selective and Collaborative Influence Function
(SCIF). Our proposed method can (i) avoid any kind of retraining which is
computationally prohibitive for large-scale systems, (ii) further enhance
efficiency by selectively updating user embedding and (iii) preserve the
collaboration across the remaining users and items. Furthermore, in order to
evaluate the unlearning completeness, we define a Membership Inference Oracle
(MIO), which can justify whether the unlearned data points were in the training
set of the model, i.e., whether a data point was completely unlearned.
Extensive experiments on two benchmark datasets demonstrate that our proposed
method can not only greatly enhance unlearning efficiency, but also achieve
adequate unlearning completeness. More importantly, our proposed method
outperforms the state-of-the-art unlearning method regarding comprehensive
recommendation metrics.
