1 citations · 1 across the 2 of their papers we have counts for
10 papers
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…
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…
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…
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…
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…