no code implementations • 11 Mar 2024 • Chaochao Chen, Yizhao Zhang, Yuyuan Li, Dan Meng, Jun Wang, Xiaoli Zheng, Jianwei Yin
The first component is distinguishability loss, where we design a distribution-based measurement to make attribute labels indistinguishable from attackers.
no code implementations • 6 Oct 2023 • Yuyuan Li, Chaochao Chen, Xiaolin Zheng, Yizhao Zhang, Zhongxuan Han, Dan Meng, Jun Wang
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.
no code implementations • 20 Apr 2023 • Yuyuan Li, Chaochao Chen, Xiaolin Zheng, Yizhao Zhang, Biao Gong, Jun Wang
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.