About me
Hello! I’m a postdoctoral researcher at Politecnico di Milano, affiliated with the RecSys@PoliMi group. If you are a student interested in working with us please refer to the Work With Us page.
Research interests
My current research field is Recommender Systems and I am working on the evaluation of deep-learning algorithms focusing on the reproducibility of published experimental results. I am also working on Applied Quantum Machine Learning for RecSys applications.
Phd Course Our PhD course on Applied Quantum Machine Learning at Politecnico di Milano is about to begin!
Published on ACM TOIS! Our paper “A Troubling Analysis of Reproducibility and Progress in Recommender Systems Research” has been published on ACM TOIS!
Short article selection
Maurizio Ferrari Dacrema, Simone Boglio, Paolo Cremonesi, Dietmar Jannach. “A Troubling Analysis of Reproducibility and Progress in Recommender Systems Research”, ACM TOIS 2021. (PDF open)
Maurizio Ferrari Dacrema, Paolo Cremonesi, Dietmar Jannach. “Are we really making much progress? A worrying analysis of recent neural recommendation approaches”, RecSys 2019. **Best Long Paper Award** . (PDF open) [BibTex]
Maurizio Ferrari Dacrema, Paolo Cremonesi, Dietmar Jannach. “Methodological Issues in Recommender Systems Research (Extended Abstract)”, IJCAI 2020. Invited Conference Paper . (PDF open) [BibTex]
Yashar Deldjoo, Maurizio Ferrari Dacrema, Mihai Gabriel Constantin, Hamid Eghbal-Zadeh, Stefano Cereda, Markus Schedl, Bogdan Ionescu, Paolo Cremonesi. “Movie Genome: Alleviating New Item Cold Start in Movie Recommendation”, UMUAI 2019. [BibTex]
Awards
- Best Long Paper Award at RecSys 2019 for “Are we really making much progress? A worrying analysis of recent neural recommendation approaches”
- Nomination for Best reviewer at RecSys 2020 and RecSys 2019.
- 2018 RecSys Challenge, 2nd place Creative Track, 4th place Main Track, team Creamy Fireflies
Program Committee
- Conferences: CIKM, KDD, WWW, RecSys
- Journals: ACM TOIS, Future Generation Computer Systems, Neurocomputing, IEEE TETC