merrafelice Goto Github PK
Name: Felice Antonio Merra, PhD
Type: User
Company: Amazon
Bio: Applied Scientist II at Amazon.
Twitter: merrafelice
Location: Berlin
Felice Antonio Merra, PhD's Projects
adversarial-machine-learning-in-recommender-systems
In this survey, we provide an exhaustive literature review of 76 articles published in major RS and ML journals and conferences. This review serves as a reference for the RS community, working on the security of RS or on generative models using GANs to improve their quality.
adversarial_personalized_ranking
Adversarial Learning, Matrix Factorization, Recommendation
assessing-perceptual-and-recommendation-mutation-of-adversarially-poisoned-visual-recommenders
In this work, we provide 24 combinations of attack/defense strategies, and visual-based recommenders to 1) access performance alteration on recommendation and 2) empirically verify the effect on final users through offline visual metrics.
code
A Black-Box Attack Model for Visually-AwareRecommender Systems - Source Code
elliot_eval
Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation
handson-recsys2020
Live coding session presented at RecSys 2020
kgs-relatedeness-recsys
Custom implementation of the paper Path-based Semantic Relatedness on Linked Data and its use to Word and Entity Disambiguation
learning-reliable-visual-saliency-for-model-explanations-
Custom implementation for the journal paper: Learning Reliable Visual Saliency For Model Explanations. IEEE TMM 2020
merrafelice.github.io
Felice Antonio Merra's Personal Blog. Follow my research activity! @merrafelice
preprocessing-milliondatasetsplaylist
This repository implements pre-processing operations of the MELON PLAYLIST DATASET released by Ferraro et al.
semantic-aware-shilling-attacks
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. We performed an extensive experimental evaluation in order to investigate whether SAShA is more effective than baseline attacks against CF models by taking into account the impact of various semantic features.
taamr
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 category of low recommended products (e.g., socks) are perturbed to misclassify the deep neural classifier towards the class of more recommended products (e.g., running shoes) with human-level slight images alterations.
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