activity
20192022
most citedSparse Feature Factorization for Recommender Systems with Knowledge Graphs

24 citations · 45 across the 2 of their papers we have counts for

collaborators

7 papers

cs.SI202221 cited

Link recommendations: Their impact on network structure and minorities

Antonio Ferrara, Lisette Espín-Noboa, Fariba Karimi +1

Network-based people recommendation algorithms are widely employed on the Web to suggest new connections in social media or professional platforms. While such recommendations bring…

cs.IR202124 cited

Sparse Feature Factorization for Recommender Systems with Knowledge Graphs

Vito Walter Anelli, Tommaso Di Noia, Eugenio Di Sciascio +2

Deep Learning and factorization-based collaborative filtering recommendation models have undoubtedly dominated the scene of recommender systems in recent years. However, despite th…

cs.IR2021

Elliot: a Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation

Vito Walter Anelli, Alejandro Bellogín, Antonio Ferrara +5

Recommender Systems have shown to be an effective way to alleviate the over-choice problem and provide accurate and tailored recommendations. However, the impressive number of prop…

cs.IR2021

FedeRank: User Controlled Feedback with Federated Recommender Systems

Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia +2

Recommender systems have shown to be a successful representative of how data availability can ease our everyday digital life. However, data privacy is one of the most prominent con…

cs.LG2020

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…

cs.LG2020

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…