Malitesta, Daniele

Malitesta, Daniele  

Dipartimento di Ingegneria Elettrica e dell'Informazione  

Personale esterno ed autonomi  

0000-0003-2228-0333

Mostra records
Risultati 1 - 20 di 27 (tempo di esecuzione: 0.046 secondi).
Titolo Data di pubblicazione Autori File
A Novel Evaluation Perspective on GNNs-based Recommender Systems through the Topology of the User-Item Graph 1-gen-2024 Daniele MalitestaClaudio PomoVito Walter AnelliAlberto Carlo Maria MancinoTommaso Di NoiaEugenio Di Sciascio
A Study of Defensive Methods to Protect Visual Recommendation against Adversarial Manipulation of Images 1-gen-2021 Vito Walter AnelliYashar DeldjooTommaso Di NoiaDaniele MalitestaFelice Antonio Merra
A Study on the Relative Importance of Convolutional Neural Networks in Visually-Aware Recommender Systems 1-gen-2021 Deldjoo, YDi Noia, TMalitesta, DMerra, FA
An Out-of-the-Box Application for Reproducible Graph Collaborative Filtering extending the Elliot Framework 1-gen-2023 Malitesta D.Pomo C.Anelli V. W.Di Noia T.Ferrara A.
Assessing perceptual and recommendation mutation of adversarially-poisoned visual recommenders 1-gen-2020 Vito Walter AnelliTommaso Di NoiaDaniele MalitestaFelice Antonio Merra
Auditing Consumer- and Producer-Fairness in Graph Collaborative Filtering 1-gen-2023 Anelli V. W.Deldjoo Y.Di Noia T.Malitesta D.Pomo C. +
Challenging the Myth of Graph Collaborative Filtering: a Reasoned and Reproducibility-driven Analysis 1-gen-2023 Vito Walter AnelliDaniele MalitestaClaudio PomoEugenio Di SciascioTommaso Di Noia +
DataRec: A Python Library for Standardized and Reproducible Data Management in Recommender Systems 1-gen-2025 Alberto Carlo Maria MancinoSalvatore BufiAngela Di FazioAntonio FerraraDaniele MalitestaClaudio PomoTommaso Di Noia
Deep Learning-Based Adaptive Image Compression System for a Real-World Scenario 1-gen-2020 Vito Walter AnelliYashar DeldjooTommaso Di NoiaDaniele Malitesta
Denoise to Protect: A Method to Robustify Visual Recommenders from Adversaries 1-gen-2023 Merra F. A.Anelli V. W.Di Noia T.Malitesta D.Mancino A. C. M.
Do We Really Need to Drop Items with Missing Modalities in Multimodal Recommendation? 1-gen-2024 Malitesta, DanielePomo, ClaudioDi Noia, Tommaso +
Ducho 2.0: Towards a More Up-to-Date Unified Framework for the Extraction of Multimodal Features in Recommendation 1-gen-2024 Matteo AttimonelliDanilo DaneseDaniele MalitestaClaudio PomoTommaso Di Noia +
Ducho: A Unified Framework for the Extraction of Multimodal Features in Recommendation 1-gen-2023 Malitesta, DanielePomo, ClaudioDi Noia, Tommaso +
Elliot: A Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation 1-gen-2021 Vito Walter AnelliAntonio FerraraDaniele MalitestaFelice Antonio MerraClaudio PomoTommaso Di Noia +
Examining Fairness in Graph-Based Collaborative Filtering: A Consumer and Producer Perspective 1-gen-2023 Di Palma D.Anelli V. W.Malitesta D.Pomo C.Deldjoo Y.Di Noia T. +
First International Workshop on Data Quality-Aware Multimodal Recommendation (DaQuaMRec) 1-gen-2025 Pomo, ClaudioMalitesta, DanieleMancino, Alberto Carlo MariaNawaz, Shah +
Formalizing Multimedia Recommendation through Multimodal Deep Learning 1-gen-2024 Malitesta, DanieleCornacchia, GiandomenicoPomo, ClaudioMerra, Felice AntonioDi Noia, TommasoDi Sciascio, Eugenio
Graph neural networks for recommendation leveraging multimodal information 1-gen-2024 Malitesta, Daniele
How Neighborhood Exploration influences Novelty and Diversity in Graph Collaborative Filtering 1-gen-2022 Anelli V. W.Deldjoo Y.Di Noia T.Di Sciascio E.Ferrara A.Malitesta D.Pomo C.
How to perform reproducible experiments in the ELLIOT recommendation framework: Data processing, model selection, and performance evaluation 1-gen-2021 Anelli V. W.Ferrara A.Malitesta D.Merra F. A.Pomo C.Donini F. M.Di Sciascio E.Di Noia T. +