Publications

Italic stands for corresponding author(s). For my complete publication profile, see Google Scholar.

2024

• Ducho meets Elliot: Large-scale Benchmarks for Multimodal Recommendation
  Matteo Attimonelli, Danilo Danese, Angela Di Fazio, Daniele Malitesta, Claudio Pomo, Tommaso Di Noia
  arXiv preprint
  [code]

• Dot Product is All You Need: Bridging the Gap Between Item Recommendation and Link Prediction
  Daniele Malitesta, Alberto Carlo Maria Mancino, Pasquale Minervini, Tommaso Di Noia
  arXiv preprint

• How Fair is Your Diffusion Recommender Model?
  Daniele Malitesta, Giacomo Medda, Erasmo Purificato, Ludovico Boratto, Fragkiskos D. Malliaros, Mirko Marras, Ernesto William De Luca
  arXiv preprint

• 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

• Report on the 1st International Workshop on Graph-Based Approaches in Information Retrieval (IRonGraphs 2024) at ECIR 2024
  Ludovico Boratto, Daniele Malitesta, Mirko Marras, Giacomo Medda, Cataldo Musto, Erasmo Purificato
  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]

• 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
  1st International Workshop on Graph-Based Approaches in Information Retrieval @ ECIR 2024
  [code]

• First International Workshop on Graph-Based Approaches in Information Retrieval (IRonGraphs 2024)
  Ludovico Boratto, Daniele Malitesta, Mirko Marras, Giacomo Medda, Cataldo Musto, Erasmo Purificato
  ECIR 2024

2023

• Graph Neural Networks for Recommendation: Reproducibility, Graph Topology, and Node Representation
  Daniele Malitesta, Claudio Pomo, Tommaso Di Noia
  LoG 2023
  [website][code]

• 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
  arXiv preprint
  [code][slides]

• On Popularity Bias of Multimodal-aware Recommender Systems: a Modalities-driven Analysis
  Daniele Malitesta, Giandomenico Cornacchia, Claudio Pomo, Tommaso Di Noia
  Workshop on Deep Multimodal Learning for Information Retrieval @ MM 2023
  [code]

• Challenging the Myth of Graph Collaborative Filtering: a Reasoned and Reproducibility-driven Analysis
  Vito Walter Anelli, Daniele Malitesta, Claudio Pomo, Alejandro Bellogin, Eugenio Di Sciascio, Tommaso Di Noia
  RecSys 2023
  [code][slides]

• 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
  3rd Workshop on Graph Learning Benchmarks @ KDD 2023
  [code]

• Auditing Consumer- and Producer-Fairness in Graph Collaborative Filtering
  Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Daniele Malitesta, Vincenzo Paparella, Claudio Pomo
  ECIR 2023
  [code]

• Ducho: A Unified Framework for the Extraction of Multimodal Features in Recommendation
  Daniele Malitesta, Giuseppe Gassi, Claudio Pomo, Tommaso Di Noia
  MM 2023
  [code]

• 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
  UMAP 2023
  [code]

• Disentangling the Performance Puzzle of Multimodal-aware Recommender Systems
  Daniele Malitesta, Giandomenico Cornacchia, Claudio Pomo, Tommaso Di Noia
  2nd Workshop on A Well-Rounded Evaluation of Recommender Systems @ KDD2023
  [code]

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
  Workshop on Deep Learning for Search and Recommendation @ CIKM2022
  [code]

• 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
  2nd Workshop on Multi-Objective Recommender Systems @ RecSys2022
  [code]

• Leveraging Content-Style Item Representation for Visual Recommendation
  Yashar Deldjoo, Tommaso Di Noia, Daniele Malitesta, Felice Antonio Merra
  ECIR 2022
  [code]

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
  RecSys 2021
  [code]

• 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
  Workshop on Online Misinformation- and Harm-Aware Recommender Systems @ RecSys 2021
  [code]

• 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
  IIR 2021
  [code]

• 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
  Workshop on Computer Vision for Fashion, Art, and Design @ CVPR 2021
  [code]

• 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
  SIGIR 2021
  [code]

• 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
  SIGIR 2021
  [code]

2020

• Assessing Perceptual and Recommendation Mutation of Adversarially-Poisoned Visual Recommenders
  Vito Walter Anelli, Tommaso Di Noia, Daniele Malitesta, Felice Antonio Merra
  Workshop on Dataset Curation and Security @ NeurIPS 2020
  [code]

• An Empirical Study of DNNs Robustification Inefficacy in Protecting Visual Recommenders
  Vito Walter Anelli, Tommaso Di Noia, Daniele Malitesta, Felice Antonio Merra
  arXiv preprint

• TAaMR: Targeted Adversarial Attack against Multimedia Recommender Systems
  Tommaso Di Noia, Daniele Malitesta, Felice Antonio Merra
  Workshop on Dependable and Secure Machine Learning @ IEEE/IFIP DSN 2020
  [code]

• Deep Learning-Based Adaptive Image Compression System for a Real-World Scenario
  Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Daniele Malitesta
  EAIS 2020