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ORCIDDaniele Malitesta
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2020 – today
- 2024
[j2]Ludovico Boratto, Daniele Malitesta, Mirko Marras, Giacomo Medda, Cataldo Musto, Erasmo Purificato:
Report on the 1st International Workshop on Graph-Based Approaches in Information Retrieval (IRonGraphs 2024) at ECIR 2024. SIGIR Forum 58(1): 1-4 (2024)- [j1]Daniele Malitesta:
Graph Neural Networks for Recommendation Leveraging Multimodal Information. SIGIR Forum 58(1): 1-2 (2024) - [c26]Ludovico Boratto
, Daniele Malitesta, Mirko Marras, Giacomo Medda, Cataldo Musto, Erasmo Purificato:
First International Workshop on Graph-Based Approaches in Information Retrieval (IRonGraphs 2024). ECIR (5) 2024: 415-421 - [c25]George Panagopoulos, Daniele Malitesta, Fragkiskos D. Malliaros, Jun Pang:
Uplift Modeling Under Limited Supervision. ECML/PKDD (6) 2024: 127-144 - [c24]Daniele Malitesta, Claudio Pomo, Vito Walter Anelli, Alberto Carlo Maria Mancino, Tommaso Di Noia, Eugenio Di Sciascio:
A Novel Evaluation Perspective on GNNs-based Recommender Systems through the Topology of the User-Item Graph. RecSys 2024: 549-559 - [c23]Matteo Attimonelli, Danilo Danese, Daniele Malitesta, Claudio Pomo, Giuseppe Gassi, Tommaso Di Noia:
Ducho 2.0: Towards a More Up-to-Date Unified Framework for the Extraction of Multimodal Features in Recommendation. WWW (Companion Volume) 2024: 1075-1078 - [i17]Matteo Attimonelli, Danilo Danese, Daniele Malitesta, Claudio Pomo, Giuseppe Gassi, Tommaso Di Noia:
Ducho 2.0: Towards a More Up-to-Date Unified Framework for the Extraction of Multimodal Features in Recommendation. CoRR abs/2403.04503 (2024) - [i16]George Panagopoulos, Daniele Malitesta, Fragkiskos D. Malliaros, Jun Pang:
Graph Neural Networks for Treatment Effect Prediction. CoRR abs/2403.19289 (2024) - [i15]Daniele Malitesta, Emanuele Rossi, Claudio Pomo, Fragkiskos D. Malliaros, Tommaso Di Noia:
Dealing with Missing Modalities in Multimodal Recommendation: a Feature Propagation-based Approach. CoRR abs/2403.19841 (2024) - [i14]Salvatore Bufi, Alberto Carlo Maria Mancino, Antonio Ferrara, Daniele Malitesta, Tommaso Di Noia, Eugenio Di Sciascio:
KGUF: Simple Knowledge-aware Graph-based Recommender with User-based Semantic Features Filtering. CoRR abs/2403.20095 (2024) - [i13]Daniele Malitesta, Claudio Pomo, Vito Walter Anelli, Alberto Carlo Maria Mancino, Tommaso Di Noia, Eugenio Di Sciascio:
A Novel Evaluation Perspective on GNNs-based Recommender Systems through the Topology of the User-Item Graph. CoRR abs/2408.11762 (2024) - [i12]Daniele Malitesta, Emanuele Rossi, Claudio Pomo, Tommaso Di Noia, Fragkiskos D. Malliaros:
Do We Really Need to Drop Items with Missing Modalities in Multimodal Recommendation? CoRR abs/2408.11767 (2024) - [i11]Daniele Malitesta, Giacomo Medda, Erasmo Purificato, Ludovico Boratto, Fragkiskos D. Malliaros, Mirko Marras, Ernesto William De Luca:
How Fair is Your Diffusion Recommender Model? CoRR abs/2409.04339 (2024) - [i10]Daniele Malitesta, Alberto Carlo Maria Mancino, Pasquale Minervini, Tommaso Di Noia:
Dot Product is All You Need: Bridging the Gap Between Item Recommendation and Link Prediction. CoRR abs/2409.07433 (2024) - [i9]Matteo Attimonelli, Danilo Danese, Angela Di Fazio, Daniele Malitesta, Claudio Pomo, Tommaso Di Noia:
Ducho meets Elliot: Large-scale Benchmarks for Multimodal Recommendation. CoRR abs/2409.15857 (2024) - 2023
- [c22]Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Daniele Malitesta, Vincenzo Paparella, Claudio Pomo:
Auditing Consumer- and Producer-Fairness in Graph Collaborative Filtering. ECIR (1) 2023: 33-48 - [c21]Dario Di Palma, Vito Walter Anelli, Daniele Malitesta, Vincenzo Paparella, Claudio Pomo, Yashar Deldjoo, Tommaso Di Noia:
Examining Fairness in Graph-Based Collaborative Filtering: A Consumer and Producer Perspective. IIR 2023: 79-84 - [c20]Daniele Malitesta, Giandomenico Cornacchia, Claudio Pomo, Tommaso Di Noia:
Disentangling the Performance Puzzle of Multimodal-aware Recommender Systems. EvalRS@KDD 2023 - [c19]Daniele Malitesta, Giuseppe Gassi, Claudio Pomo, Tommaso Di Noia:
Ducho: A Unified Framework for the Extraction of Multimodal Features in Recommendation. ACM Multimedia 2023: 9668-9671 - [c18]Daniele Malitesta, Giandomenico Cornacchia, Claudio Pomo, Tommaso Di Noia:
On Popularity Bias of Multimodal-aware Recommender Systems: A Modalities-driven Analysis. MMIR@MM 2023: 59-68 - [c17]Vito Walter Anelli, Daniele Malitesta, Claudio Pomo, Alejandro Bellogín, Eugenio Di Sciascio, Tommaso Di Noia:
Challenging the Myth of Graph Collaborative Filtering: a Reasoned and Reproducibility-driven Analysis. RecSys 2023: 350-361 - [c16]Alberto Carlo Maria Mancino, Antonio Ferrara, Salvatore Bufi, Daniele Malitesta, Tommaso Di Noia, Eugenio Di Sciascio:
KGTORe: Tailored Recommendations through Knowledge-aware GNN Models. RecSys 2023: 576-587 - [c15]Felice Antonio Merra, Vito Walter Anelli, Tommaso Di Noia, Daniele Malitesta, Alberto Carlo Maria Mancino:
Denoise to Protect: A Method to Robustify Visual Recommenders from Adversaries. SIGIR 2023: 1924-1928 - [c14]Daniele Malitesta, Claudio Pomo, Vito Walter Anelli, Tommaso Di Noia, Antonio Ferrara:
An Out-of-the-Box Application for Reproducible Graph Collaborative Filtering extending the Elliot Framework. UMAP (Adjunct Publication) 2023: 12-15 - [i8]Daniele Malitesta, Giuseppe Gassi, Claudio Pomo, Tommaso Di Noia:
Ducho: A Unified Framework for the Extraction of Multimodal Features in Recommendation. CoRR abs/2306.17125 (2023) - [i7]Vito Walter Anelli, Daniele Malitesta, Claudio Pomo, Alejandro Bellogín, Tommaso Di Noia, Eugenio Di Sciascio:
Challenging the Myth of Graph Collaborative Filtering: a Reasoned and Reproducibility-driven Analysis. CoRR abs/2308.00404 (2023) - [i6]Daniele Malitesta, Claudio Pomo, Vito Walter Anelli, Alberto Carlo Maria Mancino, Eugenio Di Sciascio, Tommaso Di Noia:
A Topology-aware Analysis of Graph Collaborative Filtering. CoRR abs/2308.10778 (2023) - [i5]Daniele Malitesta, Giandomenico Cornacchia, Claudio Pomo, Tommaso Di Noia:
On Popularity Bias of Multimodal-aware Recommender Systems: a Modalities-driven Analysis. CoRR abs/2308.12911 (2023) - [i4]Daniele Malitesta, Giandomenico Cornacchia, Claudio Pomo, Felice Antonio Merra, Tommaso Di Noia, Eugenio Di Sciascio:
Formalizing Multimedia Recommendation through Multimodal Deep Learning. CoRR abs/2309.05273 (2023) - [i3]Daniele Malitesta, Claudio Pomo, Tommaso Di Noia:
Graph Neural Networks for Recommendation: Reproducibility, Graph Topology, and Node Representation. CoRR abs/2310.11270 (2023) - 2022
- [c13]Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Eugenio Di Sciascio, Antonio Ferrara, Daniele Malitesta, Claudio Pomo:
Reshaping Graph Recommendation with Edge Graph Collaborative Filtering and Customer Reviews. DL4SR@CIKM 2022 - [c12]Yashar Deldjoo, Tommaso Di Noia, Daniele Malitesta, Felice Antonio Merra:
Leveraging Content-Style Item Representation for Visual Recommendation. ECIR (2) 2022: 84-92 - [c11]Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Eugenio Di Sciascio, Antonio Ferrara, Daniele Malitesta, Claudio Pomo:
How Neighborhood Exploration influences Novelty and Diversity in Graph Collaborative Filtering. MORS@RecSys 2022 - [c10]Vito Walter Anelli, Alejandro Bellogín, Antonio Ferrara, Daniele Malitesta, Felice Antonio Merra, Claudio Pomo, Francesco M. Donini, Eugenio Di Sciascio, Tommaso Di Noia:
The Challenging Reproducibility Task in Recommender Systems Research between Traditional and Deep Learning Models. SEBD 2022: 514-521 - 2021
- [c9]Yashar Deldjoo, Tommaso Di Noia, Daniele Malitesta, Felice Antonio Merra:
A Study on the Relative Importance of Convolutional Neural Networks in Visually-Aware Recommender Systems. CVPR Workshops 2021: 3961-3967 - [c8]Vito Walter Anelli, Alejandro Bellogín, Antonio Ferrara, Daniele Malitesta, Felice Antonio Merra, Claudio Pomo, Francesco Maria Donini, Eugenio Di Sciascio, Tommaso Di Noia:
How to Perform Reproducible Experiments in the ELLIOT Recommendation Framework: Data Processing, Model Selection, and Performance Evaluation. IIR 2021 - [c7]Vito Walter Anelli, Tommaso Di Noia, Eugenio Di Sciascio, Daniele Malitesta, Felice Antonio Merra:
Adversarial Attacks against Visual Recommendation: an Investigation on the Influence of Items' Popularity. OHARS@RecSys 2021: 33-44 - [c6]Vito Walter Anelli, Alejandro Bellogín, Antonio Ferrara, Daniele Malitesta, Felice Antonio Merra, Claudio Pomo, Francesco Maria Donini, Tommaso Di Noia:
V-Elliot: Design, Evaluate and Tune Visual Recommender Systems. RecSys 2021: 768-771 - [c5]Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Daniele Malitesta, Felice Antonio Merra:
A Study of Defensive Methods to Protect Visual Recommendation Against Adversarial Manipulation of Images. SIGIR 2021: 1094-1103 - [c4]Vito Walter Anelli, Alejandro Bellogín, Antonio Ferrara, Daniele Malitesta, Felice Antonio Merra, Claudio Pomo, Francesco Maria Donini, Tommaso Di Noia:
Elliot: A Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation. SIGIR 2021: 2405-2414 - [i2]Vito Walter Anelli, Alejandro Bellogín, Antonio Ferrara, Daniele Malitesta, Felice Antonio Merra, Claudio Pomo, Francesco M. Donini, Tommaso Di Noia:
Elliot: a Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation. CoRR abs/2103.02590 (2021) - 2020
- [c3]Vito Walter Anelli, Tommaso Di Noia, Daniele Malitesta, Felice Antonio Merra:
Assessing Perceptual and Recommendation Mutation of Adversarially-Poisoned Visual Recommenders (short paper). DP@AI*IA 2020: 49-56 - [c2]Tommaso Di Noia, Daniele Malitesta, Felice Antonio Merra:
TAaMR: Targeted Adversarial Attack against Multimedia Recommender Systems. DSN Workshops 2020: 1-8 - [c1]Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Daniele Malitesta:
Deep Learning-Based Adaptive Image Compression System for a Real-World Scenario. EAIS 2020: 1-8 - [i1]Vito Walter Anelli, Tommaso Di Noia, Daniele Malitesta, Felice Antonio Merra:
An Empirical Study of DNNs Robustification Inefficacy in Protecting Visual Recommenders. CoRR abs/2010.00984 (2020)
Coauthor Index
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last updated on 2024-10-23 20:36 CEST by the dblp team
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