313 citations · 750 across the 46 of their papers we have counts for
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Multi-Step Adversarial Perturbations on Recommender Systems Embeddings
Vito Walter Anelli, Alejandro Bellogín, Yashar Deldjoo +2
Recommender systems (RSs) have attained exceptional performance in learning users' preferences and helping them in finding the most suitable products. Recent advances in adversaria…
How to Put Users in Control of their Data in Federated Top-N Recommendation with Learning to Rank
Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia +2
Recommendation services are extensively adopted in several user-centered applications as a tool to alleviate the information overload problem and help users in orienteering in a va…
Prioritized Multi-Criteria Federated Learning
Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia +1
In Machine Learning scenarios, privacy is a crucial concern when models have to be trained with private data coming from users of a service, such as a recommender system, a locatio…
A survey on Adversarial Recommender Systems: from Attack/Defense strategies to Generative Adversarial Networks
Yashar Deldjoo, Tommaso Di Noia, Felice Antonio Merra
Latent-factor models (LFM) based on collaborative filtering (CF), such as matrix factorization (MF) and deep CF methods, are widely used in modern recommender systems (RS) due to t…