Scholar
Maurizio Ferrari Dacrema
Google Scholar ID: gYzXaPQAAAAJ
Assistant Professor, DEIB, Politecnico di Milano
Citations & Impact
Citations
2,064
H-index
18
i10-index
30
Publications
20
Co-authors
24
list available
Publications
Resume (English only)
Academic Achievements
- Publications:
- - “Reproducibility and Artifact Consistency of the SIGIR 2022 Recommender Systems Papers Based on Message Passing”, ACM Transactions on Information Systems (TOIS) 2025.
- - “A Troubling Analysis of Reproducibility and Progress in Recommender Systems Research”, ACM Transactions on Information Systems (TOIS) 2021.
- - “Are we really making much progress? A worrying analysis of recent neural recommendation approaches”, RecSys 2019 (Best Long Paper Award).
- - “Movie Genome: Alleviating New Item Cold Start in Movie Recommendation”, UMUAI 2019.
- Awards:
- - Best Long Paper Award at RecSys 2019 for “Are we really making much progress? A worrying analysis of recent neural recommendation approaches”
- - “Prof. Florian Daniel” Award, Best doctoral thesis in Computer Science Engineering, DEIB, Politecnico di Milano
- - First Prize on the Academic Track at the ACM RecSys Challenge, sponsored by Synerise at the 19th ACM Conference on Recommender Systems (RecSys 2025)
- - Best Academic Team at the ACM RecSys Challenge, sponsored by Ekstra Bladet at the 18th ACM Conference on Recommender Systems (RecSys 2024)
- - Best Academic Team at the ACM RecSys Challenge, sponsored by ShareChat at the 17th ACM Conference on Recommender Systems (RecSys 2023)
- - Best Academic Team at the ACM RecSys Challenge, sponsored by Twitter at the 15th ACM Conference on Recommender Systems (RecSys 2021)
- - 2nd place at the Creative Track of the ACM RecSys Challenge (4th place overall), sponsored by Spotify at the 12th ACM Conference on Recommender Systems (RecSys 2018), team Creamy Fireflies
- - Best Reviewer, 31st ACM International Conference on Information and Knowledge Management (CIKM 2022)
- - Nomination for Best reviewer at RecSys 2020 and RecSys 2019
Research Experience
- Conducts research in the field of machine learning and quantum computing at Politecnico di Milano; designs and evaluates algorithms for recommender systems; participates in multiple recommender system challenges.
Education
- Assistant Professor at the Department of Electronics Information and Bioengineering (DEIB), Politecnico di Milano, Italy (specific degree and advisor information not provided)
Background
- Trained as a computer science engineer, I work at the intersection of machine learning and quantum computing as an Assistant Professor at Politecnico di Milano, where I am a member of the RecSys group and the Quantum Computing Lab.
Miscellany
- Personal interests and other information not explicitly provided
