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1. Semantic-Aware-Shilling-Attacks Semantic-Aware-Shilling-Attacks Public

   In this paper, we introduce SAShA, a new attack strategy that leverages semantic features extracted from a knowledge graph in order to strengthen the efficacy of the attack to standard CF models. W…

   Python 5 2

2. TAaMR TAaMR Public

   Proposal of a novel adversarial attack approach, called Target Adversarial Attack against Multimedia Recommender Systems (TAaMR), to investigate the modification of MR behavior when the images of a…

   Python 2

3. sisinflab/adversarial-recommender-systems-survey sisinflab/adversarial-recommender-systems-survey Public

   The goal of this survey is two-fold: (i) to present recent advances on adversarial machine learning (AML) for the security of RS (i.e., attacking and defense recommendation models), (ii) to show an…

   158 32

4. sisinflab/elliot sisinflab/elliot Public

   Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation

   Python 278 50

5. sisinflab/MSAP sisinflab/MSAP Public

   In this work, we extend the FGSM method proposing multistep adversarial perturbation (MSAP) procedures to study the recommenders’ robustness under powerful methods. Letting fixed the perturbation m…

   Python 2 4

6. sisinflab/Visual-Adversarial-Recommendation sisinflab/Visual-Adversarial-Recommendation Public

   we present an evaluation framework, named Visual Adversarial Recommender (\var), to empirically investigate the performance of defended or undefended DNNs in various visually-aware item recommendat…

   Python 6 1