About me
Hello! I’m a Ph.D. Student at Politecnico di Milano, affiliated with the RecSys@PoliMi group.
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.
Open PhD positions available! Two fully funded PhD positions on Recommender Systems are available at the RecSys Lab at Politecnico di Miano on the following topics: Applied Quantum Machine Learning and Evaluation of Recommender Systems! You can find more details on our group website
Preprint available! Have a look at the preprint of the extended version of our reproducibility study (still under review). We welcome your feedback! “A Troubling Analysis of Reproducibility and Progress in Recommender Systems Research”
Short article selection
Maurizio Ferrari Dacrema, Paolo Cremonesi, Dietmar Jannach. “Methodological Issues in Recommender Systems Research (Extended Abstract)”, IJCAI 2020. Invited Conference Paper .
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** . (ArXiv) [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 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