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Post-Training Attribute Unlearning in Recommender Systems
The first component is distinguishability loss, where we design a distribution-based measurement to make attribute labels indistinguishable from attackers.
Making Users Indistinguishable: Attribute-wise Unlearning in Recommender Systems
To address the PoT-AU problem in recommender systems, we design a two-component loss function that consists of i) distinguishability loss: making attribute labels indistinguishable from attackers, and ii) regularization loss: preventing drastic changes in the model that result in a negative impact on recommendation performance.
Selective and Collaborative Influence Function for Efficient Recommendation Unlearning
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.
