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

[TBD] I will be giving a lecture on Multimodal Deep Learning for Recommendation at the 2024 RecSys Summer School.
[TBD] I will be giving a talk on our recent ACM TORS paper “Formalizing Multimedia Recommendation through Multimodal Deep Learning” in the series of seminars held at QIAU University.

[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!
[Jul 10, 2024] The dissertation abstract of my PhD thesis “Graph Neural Networks for Recommendation leveraging Multimodal Information” is now available in the latest issue of the SIGIR forum!
[Jun 5, 2024] Our paper “Uplift Modelling under Limited Supervision” w/ colleagues from CentraleSupélec and the University of Luxembourg has been accepted at ECML-PKDD 2024!
[Apr 29, 2024] Our paper “Formalizing Multimedia Recommendation through Multimodal Deep Learning” w/ colleagues from Politecnico di Bari is now accepted in ACM Transactions on Recommender Systems!

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

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]
Graph Neural Networks for Recommendation leveraging Multimodal Information (Dissertation Abstract)
Daniele Malitesta
SIGIR forum
Uplift Modelling under Limited Supervision
George Panagopoulos, Daniele Malitesta, Fragkiskos D. Malliaros, Jun Pang
ECML-PKDD 2024
Formalizing Multimedia Recommendation through Multimodal Deep Learning
Daniele Malitesta, Giandomenico Cornacchia, Claudio Pomo, Felice Antonio Merra, Tommaso Di Noia, Eugenio Di Sciascio
ACM Transactions on Recommender Systems
[code]
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, Claudio Pomo, Giuseppe Gassi, Tommaso Di Noia
The 2024 ACM Web Conference
[code]
Graph Neural Networks for Recommendation: Reproducibility, Graph Topology, and Node Representation
Daniele Malitesta, Claudio Pomo, Tommaso Di Noia
LoG 2023
[website][code]