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Found 5 papers, 3 papers with code
Does Knowledge Graph Really Matter for Recommender Systems?
We consider the scenarios where knowledge in a KG gets completely removed, randomly distorted and decreased, and also where recommendations are for cold-start users.
FoolSDEdit: Deceptively Steering Your Edits Towards Targeted Attribute-aware Distribution
To expose this potential vulnerability, we aim to build an adversarial attack forcing SDEdit to generate a specific data distribution aligned with a specified attribute (e. g., female), without changing the input's attribute characteristics.
FairRec: Fairness Testing for Deep Recommender Systems
Given the testing report, by adopting a simple re-ranking mitigation strategy on these identified disadvantaged groups, we show that the fairness of DRSs can be significantly improved.
Stability of Weighted Majority Voting under Estimated Weights
To formally analyse the uncertainty to the decision process, we introduce and analyse two important properties of such unbiased trust values: stability of correctness and stability of optimality.
RobOT: Robustness-Oriented Testing for Deep Learning Systems
A key part of RobOT is a quantitative measurement on 1) the value of each test case in improving model robustness (often via retraining), and 2) the convergence quality of the model robustness improvement.
Software Engineering
