activity
20212026
most citedSparse Feature Factorization for Recommender Systems with Knowledge Graphs

24 citations · 29 across the 10 of their papers we have counts for

collaborators

10 papers

cs.MA2026

Who Are You Explaining To? A Multi-Agent System for Audience-Aware XAI Narratives

Francesco Musicco, Danilo Danese, Giuseppe Fasano +3

Feature-attribution methods such as SHAP provide useful evidence about individual model predictions, but their numerical outputs are rarely sufficient for audiences with different…

cs.IR2025

On the Impact of Graph Neural Networks in Recommender Systems: A Topological Perspective

Daniele Malitesta, Claudio Pomo, Vito Walter Anelli +3

In recommender systems, user-item interactions can be modeled as a bipartite graph, where user and item nodes are connected by undirected edges. This graph-based view has motivated…

cs.IR2025

Balancing Accuracy and Novelty with Sub-Item Popularity

Chiara Mallamaci, Aleksandr Vladimirovich Petrov, Alberto Carlo Maria Mancino +3

In the realm of music recommendation, sequential recommenders have shown promise in capturing the dynamic nature of music consumption. A key characteristic of this domain is repeti…

cs.IR2024

DataRec: A Python Library for Standardized and Reproducible Data Management in Recommender Systems

Alberto Carlo Maria Mancino, Salvatore Bufi, Angela Di Fazio +4

Recommender systems have demonstrated significant impact across diverse domains, yet ensuring the reproducibility of experimental findings remains a persistent challenge. A primary…

cs.IR2024★ 1 cited

Dot Product is All You Need: Bridging the Gap Between Item Recommendation and Link Prediction

Daniele Malitesta, Alberto Carlo Maria Mancino, Pasquale Minervini +1

Item recommendation (the task of predicting if a user may interact with new items from the catalogue in a recommendation system) and link prediction (the task of identifying missin…

cs.IR2024★ 1 cited

A Novel Evaluation Perspective on GNNs-based Recommender Systems through the Topology of the User-Item Graph

Daniele Malitesta, Claudio Pomo, Vito Walter Anelli +3

Recently, graph neural networks (GNNs)-based recommender systems have encountered great success in recommendation. As the number of GNNs approaches rises, some works have started q…