24 citations · 29 across the 10 of their papers we have counts for
9 papers · 1 filter
Data Processing for Offline Evaluation in Recommender Systems: a Survey
Alberto Carlo Maria Mancino, Angela Di Fazio, Danilo Danese +5
Offline evaluation is the dominant experimental paradigm in recommender systems research, enabling reproducible and cost-effective comparisons on historical interaction data. Yet,…
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…
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…
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…
A Topology-aware Analysis of Graph Collaborative Filtering
Daniele Malitesta, Claudio Pomo, Vito Walter Anelli +3
The successful integration of graph neural networks into recommender systems (RSs) has led to a novel paradigm in collaborative filtering (CF), graph collaborative filtering (graph…
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…