#4: Adversarial Machine Learning for Recommenders with Felice Merra

In episode four my guest is Felice Merra, who is an applied scientist at Amazon. Felice obtained his PhD from Politecnico di Bari where he was a researcher at the Information Systems Lab (SisInf Lab). He investigated Security and Adversarial Machine Learning in Recommender Systems by looking at different ways to perturb interaction or content data, but also model parameters, and elaborated various defense strategies.

In episode four my guest is Felice Merra, who is an applied scientist at Amazon. Felice obtained his PhD from Politecnico di Bari where he was a researcher at the Information Systems Lab (SisInf Lab). There, he worked on Security and Adversarial Machine Learning in Recommender Systems.

We talk about different ways to perturb interaction or content data, but also model parameters, and elaborated various defense strategies.
In addition, we touch on the motivation of individuals or whole platforms to perform attacks and look at some examples that Felice has been working on throughout his research.
The overall goals of research in Adversarial Machine Learning for Recommender Systems is to identify vulnerabilities of models and systems in order to derive proper defense strategies that make systems more robust against potential attacks.
Finally, we also briefly discuss privacy-preserving learning and the challenges of further robustification of multimedia recommender systems.

Felice has published multiple papers at KDD, ECIR, SIGIR, and RecSys. He also won the Best Paper Award at KDD's workshop on Adversarial Learning Methods.

Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.

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