102 citations · 171 across the 19 of their papers we have counts for
20 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,…
Training-free Graph-based Imputation of Missing Modalities in Multimodal Recommendation
Daniele Malitesta, Emanuele Rossi, Claudio Pomo +2
Multimodal recommender systems (RSs) represent items in the catalog through multimodal data (e.g., product images and descriptions) that, in some cases, might be noisy or (even wor…
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…
ContextGNN goes to Elliot: Towards Benchmarking Relational Deep Learning for Static Link Prediction (aka Personalized Item Recommendation)
Alejandro Ariza-Casabona, Nikos Kanakaris, Daniele Malitesta
Relational deep learning (RDL) settles among the most exciting advances in machine learning for relational databases, leveraging the representational power of message passing graph…
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…
Large-scale Benchmarks for Multimodal Recommendation with Ducho
Matteo Attimonelli, Danilo Danese, Angela Di Fazio +3
The common multimodal recommendation pipeline involves (i) extracting multimodal features, (ii) refining their high-level representations to suit the recommendation task, (iii) opt…
Do We Really Need to Drop Items with Missing Modalities in Multimodal Recommendation?
Daniele Malitesta, Emanuele Rossi, Claudio Pomo +2
Generally, items with missing modalities are dropped in multimodal recommendation. However, with this work, we question this procedure, highlighting that it would further damage th…
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…
Dealing with Missing Modalities in Multimodal Recommendation: a Feature Propagation-based Approach
Daniele Malitesta, Emanuele Rossi, Claudio Pomo +2
Multimodal recommender systems work by augmenting the representation of the products in the catalogue through multimodal features extracted from images, textual descriptions, or au…
Ducho 2.0: Towards a More Up-to-Date Unified Framework for the Extraction of Multimodal Features in Recommendation
Matteo Attimonelli, Danilo Danese, Daniele Malitesta +3
In this work, we introduce Ducho 2.0, the latest stable version of our framework. Differently from Ducho, Ducho 2.0 offers a more personalized user experience with the definition a…
Graph Neural Networks for Recommendation: Reproducibility, Graph Topology, and Node Representation
Daniele Malitesta, Claudio Pomo, Tommaso Di Noia
Graph neural networks (GNNs) have gained prominence in recommendation systems in recent years. By representing the user-item matrix as a bipartite and undirected graph, GNNs have d…
Formalizing Multimedia Recommendation through Multimodal Deep Learning
Daniele Malitesta, Giandomenico Cornacchia, Claudio Pomo +3
Recommender systems (RSs) offer personalized navigation experiences on online platforms, but recommendation remains a challenging task, particularly in specific scenarios and domai…
On Popularity Bias of Multimodal-aware Recommender Systems: a Modalities-driven Analysis
Daniele Malitesta, Giandomenico Cornacchia, Claudio Pomo +1
Multimodal-aware recommender systems (MRSs) exploit multimodal content (e.g., product images or descriptions) as items' side information to improve recommendation accuracy. While m…
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…
Challenging the Myth of Graph Collaborative Filtering: a Reasoned and Reproducibility-driven Analysis
Vito Walter Anelli, Daniele Malitesta, Claudio Pomo +3
The success of graph neural network-based models (GNNs) has significantly advanced recommender systems by effectively modeling users and items as a bipartite, undirected graph. How…
Ducho: A Unified Framework for the Extraction of Multimodal Features in Recommendation
Daniele Malitesta, Giuseppe Gassi, Claudio Pomo +1
In multimodal-aware recommendation, the extraction of meaningful multimodal features is at the basis of high-quality recommendations. Generally, each recommendation framework imple…
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…
An Empirical Study of DNNs Robustification Inefficacy in Protecting Visual Recommenders
Vito Walter Anelli, Tommaso Di Noia, Daniele Malitesta +1
Visual-based recommender systems (VRSs) enhance recommendation performance by integrating users' feedback with the visual features of product images extracted from a deep neural ne…