  With the growing privacy concerns in recommender systems, recommendation
unlearning is getting increasing attention. Existing studies predominantly use
training data, i.e., model inputs, as unlearning target. However, attackers can
extract private information from the model even if it has not been explicitly
encountered during training. We name this unseen information as
\textit{attribute} and treat it as unlearning target. To protect the sensitive
attribute of users, Attribute Unlearning (AU) aims to make target attributes
indistinguishable. In this paper, we focus on a strict but practical setting of
AU, namely Post-Training Attribute Unlearning (PoT-AU), where unlearning can
only be performed after the training of the recommendation model is completed.
To address the PoT-AU problem in recommender systems, we propose a
two-component loss function. The first component is distinguishability loss,
where we design a distribution-based measurement to make attribute labels
indistinguishable from attackers. We further extend this measurement to handle
multi-class attribute cases with efficient computational overhead. The second
component is regularization loss, where we explore a function-space measurement
that effectively maintains recommendation performance compared to
parameter-space regularization. We use stochastic gradient descent algorithm to
optimize our proposed loss. Extensive experiments on four real-world datasets
demonstrate the effectiveness of our proposed methods.
