24 citations · 29 across the 10 of their papers we have counts for
4 papers · 2 filters
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…
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…
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…
KGUF: Simple Knowledge-aware Graph-based Recommender with User-based Semantic Features Filtering
Salvatore Bufi, Alberto Carlo Maria Mancino, Antonio Ferrara +3
The recent integration of Graph Neural Networks (GNNs) into recommendation has led to a novel family of Collaborative Filtering (CF) approaches, namely Graph Collaborative Filterin…