Daniele Malitesta

According to our database, Daniele Malitesta authored at least 31 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

KGUF: Simple Knowledge-aware Graph-based Recommender with User-based Semantic Features Filtering.

Salvatore Bufi, Alberto Carlo Maria Mancino, Antonio Ferrara, Daniele Malitesta, Tommaso Di Noia, Eugenio Di Sciascio

CoRR, 2024

Dealing with Missing Modalities in Multimodal Recommendation: a Feature Propagation-based Approach.

Daniele Malitesta, Emanuele Rossi, Claudio Pomo, Fragkiskos D. Malliaros, Tommaso Di Noia

CoRR, 2024

Graph Neural Networks for Treatment Effect Prediction.

George Panagopoulos, Daniele Malitesta, Fragkiskos D. Malliaros, Jun Pang

CoRR, 2024

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

CoRR, 2024

First International Workshop on Graph-Based Approaches in Information Retrieval (IRonGraphs 2024).

Ludovico Boratto, Daniele Malitesta, Mirko Marras, Giacomo Medda, Cataldo Musto, Erasmo Purificato

Proceedings of the Advances in Information Retrieval, 2024

2023

Graph Neural Networks for Recommendation: Reproducibility, Graph Topology, and Node Representation.

Daniele Malitesta, Claudio Pomo, Tommaso Di Noia

CoRR, 2023

Formalizing Multimedia Recommendation through Multimodal Deep Learning.

Daniele Malitesta, Giandomenico Cornacchia, Claudio Pomo, Felice Antonio Merra, Tommaso Di Noia, Eugenio Di Sciascio

CoRR, 2023

A Topology-aware Analysis of Graph Collaborative Filtering.

Daniele Malitesta, Claudio Pomo, Vito Walter Anelli, Alberto Carlo Maria Mancino, Eugenio Di Sciascio, Tommaso Di Noia

CoRR, 2023

An Out-of-the-Box Application for Reproducible Graph Collaborative Filtering extending the Elliot Framework.

Daniele Malitesta, Claudio Pomo, Vito Walter Anelli, Tommaso Di Noia, Antonio Ferrara

Proceedings of the Adjunct Proceedings of the 31st ACM Conference on User Modeling, 2023

Denoise to Protect: A Method to Robustify Visual Recommenders from Adversaries.

Felice Antonio Merra, Vito Walter Anelli, Tommaso Di Noia, Daniele Malitesta, Alberto Carlo Maria Mancino

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023

KGTORe: Tailored Recommendations through Knowledge-aware GNN Models.

Alberto Carlo Maria Mancino, Antonio Ferrara, Salvatore Bufi, Daniele Malitesta, Tommaso Di Noia, Eugenio Di Sciascio

Proceedings of the 17th ACM Conference on Recommender Systems, 2023

Challenging the Myth of Graph Collaborative Filtering: a Reasoned and Reproducibility-driven Analysis.

Vito Walter Anelli, Daniele Malitesta, Claudio Pomo, Alejandro Bellogín, Eugenio Di Sciascio, Tommaso Di Noia

Proceedings of the 17th ACM Conference on Recommender Systems, 2023

On Popularity Bias of Multimodal-aware Recommender Systems: A Modalities-driven Analysis.

Daniele Malitesta, Giandomenico Cornacchia, Claudio Pomo, Tommaso Di Noia

Proceedings of the 1st International Workshop on Deep Multimodal Learning for Information Retrieval, 2023

Ducho: A Unified Framework for the Extraction of Multimodal Features in Recommendation.

Daniele Malitesta, Giuseppe Gassi, Claudio Pomo, Tommaso Di Noia

Proceedings of the 31st ACM International Conference on Multimedia, 2023

Disentangling the Performance Puzzle of Multimodal-aware Recommender Systems.

Daniele Malitesta, Giandomenico Cornacchia, Claudio Pomo, Tommaso Di Noia

Proceedings of EvalRS: A Rounded Evaluation Of Recommender Systems 2023 co-located with 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (SIGKDD 2023), 2023

Examining Fairness in Graph-Based Collaborative Filtering: A Consumer and Producer Perspective.

Dario Di Palma, Vito Walter Anelli, Daniele Malitesta, Vincenzo Paparella, Claudio Pomo, Yashar Deldjoo, Tommaso Di Noia

Proceedings of the 13th Italian Information Retrieval Workshop (IIR 2023), 2023

Auditing Consumer- and Producer-Fairness in Graph Collaborative Filtering.

Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Daniele Malitesta, Vincenzo Paparella, Claudio Pomo

Proceedings of the Advances in Information Retrieval, 2023

2022

The Challenging Reproducibility Task in Recommender Systems Research between Traditional and Deep Learning Models.

Vito Walter Anelli, Alejandro Bellogín, Antonio Ferrara, Daniele Malitesta, Felice Antonio Merra, Claudio Pomo, Francesco M. Donini, Eugenio Di Sciascio, Tommaso Di Noia

Proceedings of the 30th Italian Symposium on Advanced Database Systems, 2022

How Neighborhood Exploration influences Novelty and Diversity in Graph Collaborative Filtering.

Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Eugenio Di Sciascio, Antonio Ferrara, Daniele Malitesta, Claudio Pomo

Proceedings of the 2nd Workshop on Multi-Objective Recommender Systems co-located with 16th ACM Conference on Recommender Systems (RecSys 2022), 2022

Leveraging Content-Style Item Representation for Visual Recommendation.

Yashar Deldjoo, Tommaso Di Noia, Daniele Malitesta, Felice Antonio Merra

Proceedings of the Advances in Information Retrieval, 2022

Reshaping Graph Recommendation with Edge Graph Collaborative Filtering and Customer Reviews.

Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Eugenio Di Sciascio, Antonio Ferrara, Daniele Malitesta, Claudio Pomo

Proceedings of the Workshop on Deep Learning for Search and Recommendation (DL4SR 2022) co-located with the 31st ACM International Conference on Information and Knowledge Management (CIKM 2022), 2022

2021

A Study of Defensive Methods to Protect Visual Recommendation Against Adversarial Manipulation of Images.

Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Daniele Malitesta, Felice Antonio Merra

Proceedings of the SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021

Elliot: A Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation.

Vito Walter Anelli, Alejandro Bellogín, Antonio Ferrara, Daniele Malitesta, Felice Antonio Merra, Claudio Pomo, Francesco Maria Donini, Tommaso Di Noia

Proceedings of the SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021

Adversarial Attacks against Visual Recommendation: an Investigation on the Influence of Items' Popularity.

Vito Walter Anelli, Tommaso Di Noia, Eugenio Di Sciascio, Daniele Malitesta, Felice Antonio Merra

Proceedings of the Second Workshop on Online Misinformation- and Harm-Aware Recommender Systems co-located with 15th ACM Conference on Recommender Systems (RecSys 2021), 2021

V-Elliot: Design, Evaluate and Tune Visual Recommender Systems.

Vito Walter Anelli, Alejandro Bellogín, Antonio Ferrara, Daniele Malitesta, Felice Antonio Merra, Claudio Pomo, Francesco Maria Donini, Tommaso Di Noia

Proceedings of the RecSys '21: Fifteenth ACM Conference on Recommender Systems, Amsterdam, The Netherlands, 27 September 2021, 2021

How to Perform Reproducible Experiments in the ELLIOT Recommendation Framework: Data Processing, Model Selection, and Performance Evaluation.

Vito Walter Anelli, Alejandro Bellogín, Antonio Ferrara, Daniele Malitesta, Felice Antonio Merra, Claudio Pomo, Francesco Maria Donini, Eugenio Di Sciascio, Tommaso Di Noia

Proceedings of the 11th Italian Information Retrieval Workshop 2021, 2021

A Study on the Relative Importance of Convolutional Neural Networks in Visually-Aware Recommender Systems.

Yashar Deldjoo, Tommaso Di Noia, Daniele Malitesta, Felice Antonio Merra

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2021

2020

An Empirical Study of DNNs Robustification Inefficacy in Protecting Visual Recommenders.

Vito Walter Anelli, Tommaso Di Noia, Daniele Malitesta, Felice Antonio Merra

CoRR, 2020

Deep Learning-Based Adaptive Image Compression System for a Real-World Scenario.

Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Daniele Malitesta

Proceedings of the 2020 IEEE Conference on Evolving and Adaptive Intelligent Systems, 2020

TAaMR: Targeted Adversarial Attack against Multimedia Recommender Systems.

Tommaso Di Noia, Daniele Malitesta, Felice Antonio Merra

Proceedings of the 50th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops, 2020

Assessing Perceptual and Recommendation Mutation of Adversarially-Poisoned Visual Recommenders (short paper).

Vito Walter Anelli, Tommaso Di Noia, Daniele Malitesta, Felice Antonio Merra

Proceedings of the AIxIA 2020 Discussion Papers Workshop co-located with the the 19th International Conference of the Italian Association for Artificial Intelligence (AIxIA2020), 2020