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Found 5 papers, 2 papers with code
Enhancing Attributed Graph Networks with Alignment and Uniformity Constraints for Session-based Recommendation
In this paper, we propose a model-agnostic framework, named AttrGAU (Attributed Graph Networks with Alignment and Uniformity Constraints), to bring the MIA's superiority into existing attribute-agnostic models, to improve their accuracy and robustness for recommendation.
CURE4Rec: A Benchmark for Recommendation Unlearning with Deeper Influence
Specifically, we consider the deeper influence of unlearning on recommendation fairness and robustness towards data with varying impact levels.
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.
