Daniele Malitesta

63 papers A* 5A 10Journal 27Unranked 19

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Large-scale benchmarks for multimodal recommendation with Ducho. Expert Syst. Appl. Matteo Attimonelli, Danilo Danese, Angela Di Fazio, Daniele Malitesta, Claudio Pomo, Tommaso Di Noia
2026	—	`ed.`	Proceedings of the DaQuaMRec 2025 Workshop on Data Quality-Aware Multimodal Recommendation co-located with RecSys 2025, Prague, Czech Republic, September 22, 2025. DaQuaMRec@RecSys Claudio Pomo, Dietmar Jannach, Yubin Kim, Daniele Malitesta, Alberto Carlo Maria Mancino, Julian J. McAuley, Alessandro B. Melchiorre, Shah Nawaz
2026	J	`jnl`	Training-Free Graph-Based Imputation of Missing Modalities in Multimodal Recommendation. IEEE Trans. Knowl. Data Eng. Daniele Malitesta, Emanuele Rossi, Claudio Pomo, Tommaso Di Noia, Fragkiskos D. Malliaros
2026	J	`jnl`	Training-free Graph-based Imputation of Missing Modalities in Multimodal Recommendation. CoRR Daniele Malitesta, Emanuele Rossi, Claudio Pomo, Tommaso Di Noia, Fragkiskos D. Malliaros
2025	—	`ed.`	Advances on Graph-Based Approaches in Information Retrieval - First International Workshop, IRonGraphs 2024, Glasgow, UK, March 24, 2024, Proceedings IRonGraphs Ludovico Boratto, Daniele Malitesta, Mirko Marras, Giacomo Medda, Cataldo Musto, Erasmo Purificato
2025	J	`jnl`	ContextGNN goes to Elliot: Towards Benchmarking Relational Deep Learning for Static Link Prediction (aka Personalized Item Recommendation). CoRR Alejandro Ariza-Casabona, Nikos Kanakaris, Daniele Malitesta
2025	A*	`conf`	DataRec: A Python Library for Standardized and Reproducible Data Management in Recommender Systems. SIGIR Alberto Carlo Maria Mancino, Salvatore Bufi, Angela Di Fazio, Antonio Ferrara, Daniele Malitesta, Claudio Pomo, Tommaso Di Noia
2025	J	`jnl`	FAROS: Fair Graph Generation via Attribute Switching Mechanisms. CoRR Abdennacer Badaoui, Oussama Kharouiche, Hatim Mrabet, Daniele Malitesta, Fragkiskos D. Malliaros
2025	A	`conf`	First International Workshop on Data Quality-Aware Multimodal Recommendation (DaQuaMRec). RecSys Claudio Pomo, Dietmar Jannach, Yubin Kim, Daniele Malitesta, Alberto Carlo Maria Mancino, Julian J. McAuley, Alessandro B. Melchiorre, Shah Nawaz
2025	J	`jnl`	Formalizing Multimedia Recommendation through Multimodal Deep Learning. Trans. Recomm. Syst. Daniele Malitesta, Giandomenico Cornacchia, Claudio Pomo, Felice Antonio Merra, Tommaso Di Noia, Eugenio Di Sciascio
2025	A	`conf`	How Fair is Your Diffusion Recommender Model? RecSys Daniele Malitesta, Giacomo Medda, Erasmo Purificato, Mirko Marras, Fragkiskos D. Malliaros, Ludovico Boratto
2025	A	`conf`	How Powerful are LLMs to Support Multimodal Recommendation? A Reproducibility Study of LLMRec. RecSys Maria Lucia Fioretti, Nicola Laterza, Alessia Preziosa, Daniele Malitesta, Claudio Pomo, Fedelucio Narducci, Tommaso Di Noia
2025	J	`jnl`	On the Impact of Graph Neural Networks in Recommender Systems: A Topological Perspective. CoRR Daniele Malitesta, Claudio Pomo, Vito Walter Anelli, Alberto Carlo Maria Mancino, Alejandro Bellogín, Tommaso Di Noia
2025	A	`conf`	SmaLLEXT: 1st Workshop on Small and Efficient Large Language Models for Knowledge Extraction. CIKM Felice Antonio Merra, Kristian Skracic, Daniele Malitesta, Jacek Golebiowski, Pasquale Minervini
2025	A	`conf`	Standard Practices for Data Processing and Multimodal Feature Extraction in Recommendation with DataRec and Ducho (D&D4Rec). RecSys Alberto Carlo Maria Mancino, Matteo Attimonelli, Angela Di Fazio, Daniele Malitesta, Tommaso Di Noia
2024	A	`conf`	A Novel Evaluation Perspective on GNNs-based Recommender Systems through the Topology of the User-Item Graph. RecSys Daniele Malitesta, Claudio Pomo, Vito Walter Anelli, Alberto Carlo Maria Mancino, Tommaso Di Noia, Eugenio Di Sciascio
2024	J	`jnl`	A Novel Evaluation Perspective on GNNs-based Recommender Systems through the Topology of the User-Item Graph. CoRR Daniele Malitesta, Claudio Pomo, Vito Walter Anelli, Alberto Carlo Maria Mancino, Tommaso Di Noia, Eugenio Di Sciascio
2024	J	`jnl`	DataRec: A Framework for Standardizing Recommendation Data Processing and Analysis. CoRR Alberto Carlo Maria Mancino, Salvatore Bufi, Angela Di Fazio, Daniele Malitesta, Claudio Pomo, Antonio Ferrara, Tommaso Di Noia
2024	J	`jnl`	Dealing with Missing Modalities in Multimodal Recommendation: a Feature Propagation-based Approach. CoRR Daniele Malitesta, Emanuele Rossi, Claudio Pomo, Fragkiskos D. Malliaros, Tommaso Di Noia
2024	A	`conf`	Do We Really Need to Drop Items with Missing Modalities in Multimodal Recommendation? CIKM Daniele Malitesta, Emanuele Rossi, Claudio Pomo, Tommaso Di Noia, Fragkiskos D. Malliaros
2024	J	`jnl`	Do We Really Need to Drop Items with Missing Modalities in Multimodal Recommendation? CoRR Daniele Malitesta, Emanuele Rossi, Claudio Pomo, Tommaso Di Noia, Fragkiskos D. Malliaros
2024	J	`jnl`	Dot Product is All You Need: Bridging the Gap Between Item Recommendation and Link Prediction. CoRR Daniele Malitesta, Alberto Carlo Maria Mancino, Pasquale Minervini, Tommaso Di Noia
2024	—	`conf`	Ducho 2.0: Towards a More Up-to-Date Unified Framework for the Extraction of Multimodal Features in Recommendation. WWW (Companion Volume) Matteo Attimonelli, Danilo Danese, Daniele Malitesta, Claudio Pomo, Giuseppe Gassi, Tommaso Di Noia
2024	J	`jnl`	Ducho 2.0: Towards a More Up-to-Date Unified Framework for the Extraction of Multimodal Features in Recommendation. CoRR Matteo Attimonelli, Danilo Danese, Daniele Malitesta, Claudio Pomo, Giuseppe Gassi, Tommaso Di Noia
2024	J	`jnl`	Ducho meets Elliot: Large-scale Benchmarks for Multimodal Recommendation. CoRR Matteo Attimonelli, Danilo Danese, Angela Di Fazio, Daniele Malitesta, Claudio Pomo, Tommaso Di Noia
2024	—	`conf`	First International Workshop on Graph-Based Approaches in Information Retrieval (IRonGraphs 2024). ECIR (5) Ludovico Boratto, Daniele Malitesta, Mirko Marras, Giacomo Medda, Cataldo Musto, Erasmo Purificato
2024	J	`jnl`	Graph Neural Networks for Recommendation Leveraging Multimodal Information. SIGIR Forum Daniele Malitesta
2024	J	`jnl`	Graph Neural Networks for Treatment Effect Prediction. CoRR George Panagopoulos, Daniele Malitesta, Fragkiskos D. Malliaros, Jun Pang
2024	J	`jnl`	How Fair is Your Diffusion Recommender Model? CoRR Daniele Malitesta, Giacomo Medda, Erasmo Purificato, Ludovico Boratto, Fragkiskos D. Malliaros, Mirko Marras, Ernesto William De Luca
2024	—	`conf`	KGUF: Simple Knowledge-Aware Graph-Based Recommender with User-Based Semantic Features Filtering. IRonGraphs Salvatore Bufi, Alberto Carlo Maria Mancino, Antonio Ferrara, Daniele Malitesta, Tommaso Di Noia, Eugenio Di Sciascio
2024	J	`jnl`	KGUF: Simple Knowledge-aware Graph-based Recommender with User-based Semantic Features Filtering. CoRR Salvatore Bufi, Alberto Carlo Maria Mancino, Antonio Ferrara, Daniele Malitesta, Tommaso Di Noia, Eugenio Di Sciascio
2024	J	`jnl`	Report on the 1st International Workshop on Graph-Based Approaches in Information Retrieval (IRonGraphs 2024) at ECIR 2024. SIGIR Forum Ludovico Boratto, Daniele Malitesta, Mirko Marras, Giacomo Medda, Cataldo Musto, Erasmo Purificato
2024	—	`conf`	Uplift Modeling Under Limited Supervision. ECML/PKDD (6) George Panagopoulos, Daniele Malitesta, Fragkiskos D. Malliaros, Jun Pang
2023	J	`jnl`	A Topology-aware Analysis of Graph Collaborative Filtering. CoRR Daniele Malitesta, Claudio Pomo, Vito Walter Anelli, Alberto Carlo Maria Mancino, Eugenio Di Sciascio, Tommaso Di Noia
2023	—	`conf`	An Out-of-the-Box Application for Reproducible Graph Collaborative Filtering extending the Elliot Framework. UMAP (Adjunct Publication) Daniele Malitesta, Claudio Pomo, Vito Walter Anelli, Tommaso Di Noia, Antonio Ferrara
2023	—	`conf`	Auditing Consumer- and Producer-Fairness in Graph Collaborative Filtering. ECIR (1) Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Daniele Malitesta, Vincenzo Paparella, Claudio Pomo
2023	A	`conf`	Challenging the Myth of Graph Collaborative Filtering: a Reasoned and Reproducibility-driven Analysis. RecSys Vito Walter Anelli, Daniele Malitesta, Claudio Pomo, Alejandro Bellogín, Eugenio Di Sciascio, Tommaso Di Noia
2023	J	`jnl`	Challenging the Myth of Graph Collaborative Filtering: a Reasoned and Reproducibility-driven Analysis. CoRR Vito Walter Anelli, Daniele Malitesta, Claudio Pomo, Alejandro Bellogín, Tommaso Di Noia, Eugenio Di Sciascio
2023	A*	`conf`	Denoise to Protect: A Method to Robustify Visual Recommenders from Adversaries. SIGIR Felice Antonio Merra, Vito Walter Anelli, Tommaso Di Noia, Daniele Malitesta, Alberto Carlo Maria Mancino
2023	—	`conf`	Disentangling the Performance Puzzle of Multimodal-aware Recommender Systems. EvalRS@KDD Daniele Malitesta, Giandomenico Cornacchia, Claudio Pomo, Tommaso Di Noia
2023	A*	`conf`	Ducho: A Unified Framework for the Extraction of Multimodal Features in Recommendation. ACM Multimedia Daniele Malitesta, Giuseppe Gassi, Claudio Pomo, Tommaso Di Noia
2023	J	`jnl`	Ducho: A Unified Framework for the Extraction of Multimodal Features in Recommendation. CoRR Daniele Malitesta, Giuseppe Gassi, Claudio Pomo, Tommaso Di Noia
2023	—	`conf`	Examining Fairness in Graph-Based Collaborative Filtering: A Consumer and Producer Perspective. IIR Dario Di Palma, Vito Walter Anelli, Daniele Malitesta, Vincenzo Paparella, Claudio Pomo, Yashar Deldjoo, Tommaso Di Noia
2023	J	`jnl`	Formalizing Multimedia Recommendation through Multimodal Deep Learning. CoRR Daniele Malitesta, Giandomenico Cornacchia, Claudio Pomo, Felice Antonio Merra, Tommaso Di Noia, Eugenio Di Sciascio
2023	J	`jnl`	Graph Neural Networks for Recommendation: Reproducibility, Graph Topology, and Node Representation. CoRR Daniele Malitesta, Claudio Pomo, Tommaso Di Noia
2023	A	`conf`	KGTORe: Tailored Recommendations through Knowledge-aware GNN Models. RecSys Alberto Carlo Maria Mancino, Antonio Ferrara, Salvatore Bufi, Daniele Malitesta, Tommaso Di Noia, Eugenio Di Sciascio
2023	—	`conf`	On Popularity Bias of Multimodal-aware Recommender Systems: A Modalities-driven Analysis. MMIR@MM Daniele Malitesta, Giandomenico Cornacchia, Claudio Pomo, Tommaso Di Noia
2023	J	`jnl`	On Popularity Bias of Multimodal-aware Recommender Systems: a Modalities-driven Analysis. CoRR Daniele Malitesta, Giandomenico Cornacchia, Claudio Pomo, Tommaso Di Noia
2022	—	`conf`	How Neighborhood Exploration influences Novelty and Diversity in Graph Collaborative Filtering. MORS@RecSys Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Eugenio Di Sciascio, Antonio Ferrara, Daniele Malitesta, Claudio Pomo
2022	—	`conf`	Leveraging Content-Style Item Representation for Visual Recommendation. ECIR (2) Yashar Deldjoo, Tommaso Di Noia, Daniele Malitesta, Felice Antonio Merra
2022	—	`conf`	Reshaping Graph Recommendation with Edge Graph Collaborative Filtering and Customer Reviews. DL4SR@CIKM Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Eugenio Di Sciascio, Antonio Ferrara, Daniele Malitesta, Claudio Pomo
2022	—	`conf`	The Challenging Reproducibility Task in Recommender Systems Research between Traditional and Deep Learning Models. SEBD Vito Walter Anelli, Alejandro Bellogín, Antonio Ferrara, Daniele Malitesta, Felice Antonio Merra, Claudio Pomo, Francesco M. Donini, Eugenio Di Sciascio, Tommaso Di Noia
2021	A*	`conf`	A Study of Defensive Methods to Protect Visual Recommendation Against Adversarial Manipulation of Images. SIGIR Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Daniele Malitesta, Felice Antonio Merra
2021	—	`conf`	A Study on the Relative Importance of Convolutional Neural Networks in Visually-Aware Recommender Systems. CVPR Workshops Yashar Deldjoo, Tommaso Di Noia, Daniele Malitesta, Felice Antonio Merra
2021	—	`conf`	Adversarial Attacks against Visual Recommendation: an Investigation on the Influence of Items' Popularity. OHARS@RecSys Vito Walter Anelli, Tommaso Di Noia, Eugenio Di Sciascio, Daniele Malitesta, Felice Antonio Merra
2021	A*	`conf`	Elliot: A Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation. SIGIR Vito Walter Anelli, Alejandro Bellogín, Antonio Ferrara, Daniele Malitesta, Felice Antonio Merra, Claudio Pomo, Francesco Maria Donini, Tommaso Di Noia
2021	J	`jnl`	Elliot: a Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation. CoRR Vito Walter Anelli, Alejandro Bellogín, Antonio Ferrara, Daniele Malitesta, Felice Antonio Merra, Claudio Pomo, Francesco M. Donini, Tommaso Di Noia
2021	—	`conf`	How to Perform Reproducible Experiments in the ELLIOT Recommendation Framework: Data Processing, Model Selection, and Performance Evaluation. IIR Vito Walter Anelli, Alejandro Bellogín, Antonio Ferrara, Daniele Malitesta, Felice Antonio Merra, Claudio Pomo, Francesco Maria Donini, Eugenio Di Sciascio, Tommaso Di Noia
2021	A	`conf`	V-Elliot: Design, Evaluate and Tune Visual Recommender Systems. RecSys Vito Walter Anelli, Alejandro Bellogín, Antonio Ferrara, Daniele Malitesta, Felice Antonio Merra, Claudio Pomo, Francesco Maria Donini, Tommaso Di Noia
2020	J	`jnl`	An Empirical Study of DNNs Robustification Inefficacy in Protecting Visual Recommenders. CoRR Vito Walter Anelli, Tommaso Di Noia, Daniele Malitesta, Felice Antonio Merra
2020	—	`conf`	Assessing Perceptual and Recommendation Mutation of Adversarially-Poisoned Visual Recommenders (short paper). DP@AI*IA Vito Walter Anelli, Tommaso Di Noia, Daniele Malitesta, Felice Antonio Merra
2020	—	`conf`	Deep Learning-Based Adaptive Image Compression System for a Real-World Scenario. EAIS Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Daniele Malitesta
2020	—	`conf`	TAaMR: Targeted Adversarial Attack against Multimedia Recommender Systems. DSN Workshops Tommaso Di Noia, Daniele Malitesta, Felice Antonio Merra