About me

Hi there! I’m Daniele Malitesta, a postdoc researcher at CentraleSupélec, Inria (Université Paris-Saclay) working with Prof. Fragkiskos Malliaros.

Previously, I pursued my PhD in Electrical and Information Engineering at Politecnico di Bari under the supervision of Prof. Tommaso Di Noia.

My main research topics are Graph Representation Learning, Recommender Systems, and Multimodal Deep Learning.

Upcoming events

[Nov 29, 2024] Me, along with colleagues from CentraleSupélec, Ecole Polytechnique, CNRS, Télécom, and Entalpic will host the LoG 2024 Meetup in Paris. Check our event website.
[Oct 22, 2024] I will present our short paper “Do We Really Need to Drop Items with Missing Modalities in Multimodal Recommendation?” at CIKM 2024 in Boise, Idaho.
[Oct 22, 2024] I will chair the full paper session on graph-based recommendation at CIKM 2024.
[Oct 09, 2024] I will be giving a lecture on Multimodal Deep Learning for Recommendation at the 2024 RecSys Summer School.

[Oct 15, 2024] For the second time in a row, I was nominated among the outstanding reviewers at RecSys 2024!
[Oct 06, 2024] The proceedings of the IRonGraphs workshop (co-located with ECIR 2024) are finally out: check them at this link!
[Jul 26, 2024] Our paper “A Novel Evaluation Perspective on GNNs-based Recommender Systems through the Topology of the User-Item Graph” w/ colleagues from Politecnico di Bari has been accepted at RecSys 2024!
[Jul 15, 2024] Our paper “Do We Really Need to Drop Items with Missing Modalities in Multimodal Recommendation?” w/ colleagues from CentraleSupélec, Politecnico di Bari, and VantAI has been accepted at CIKM 2024!

A selection of recent publications (for the full list, please visit this link).

Ducho meets Elliot: Large-scale Benchmarks for Multimodal Recommendation
Matteo Attimonelli, Danilo Danese, Angela Di Fazio, Daniele Malitesta, Claudio Pomo, Tommaso Di Noia
arXiv preprint
[code]
Dot Product is All You Need: Bridging the Gap Between Item Recommendation and Link Prediction
Daniele Malitesta, Alberto Carlo Maria Mancino, Pasquale Minervini, Tommaso Di Noia
arXiv preprint
How Fair is Your Diffusion Recommender Model?
Daniele Malitesta, Giacomo Medda, Erasmo Purificato, Ludovico Boratto, Fragkiskos D. Malliaros, Mirko Marras, Ernesto William De Luca
arXiv preprint
A Novel Evaluation Perspective on GNNs-based Recommender Systems through the Topology of the User-Item Graph
Daniele Malitesta, Claudio Pomo, Vito Walter Anelli, Alberto Carlo Maria Mancino, Tommaso Di Noia, Eugenio Di Sciascio
RecSys 2024
[code]
Do We Really Need to Drop Items with Missing Modalities in Multimodal Recommendation?
Daniele Malitesta, Emanuele Rossi, Claudio Pomo, Tommaso Di Noia, Fragkiskos D. Malliaros
CIKM 2024
[code]
Uplift Modelling under Limited Supervision
George Panagopoulos, Daniele Malitesta, Fragkiskos D. Malliaros, Jun Pang
ECML-PKDD 2024