About me
Hello! I’m an Assistant Professor at Politecnico di Milano, affiliated with the RecSys group and the QCLab. If you are a student interested in working with us please refer to the Work With Us page.
Research interests
- Applied Quantum Computing: design, evaluation and benchmarking of machine learning algorithms on quantum computers and use of machine learning to benefit current generation quantum computing hardware
- Recommender Systems: design and evaluation of algorithms for recommender systems
Call for Paper is out! Check the call for papers of our Workshop on Learning and Evaluating Recommendations with Impressions, that will be held at RecSys2023 this September.
Accepted at NeurIPS! Our paper “Off-Policy Evaluation with Deficient Support Using Side Information” has been accepted at NeurIPS!
Published on Nature Scientific Reports! Our paper “Evaluating the job shop scheduling problem on a D-wave quantum annealer” has been published on Nature Scientific Reports!
Quantum Computing Lab Our PhD course on Applied Quantum Machine Learning at Politecnico di Milano is concluded.
Short article selection
Costantino Carugno, Maurizio Ferrari Dacrema, Paolo Cremonesi. “Evaluating the job shop scheduling problem on a D-wave quantum annealer” (PDF open). Nature Scientific Reports 2022, 1, 12. [BibTex]
Maurizio Ferrari Dacrema, Nicolò Felicioni, and Paolo Cremonesi. “Offline evaluation of recommender systems in a user interface with multiple carousels” (PDF open). Frontiers Big Data, 5, 21 pages, 2022.
Riccardo Nembrini, Maurizio Ferrari Dacrema, Paolo Cremonesi. “Feature Selection for Recommender Systems with Quantum Computing” (PDF open). Entropy 2021, 23, 970. [BibTex]
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) [BibTex]
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]
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”
- “Prof. Florian Daniel” Award, Best doctoral thesis in Computer Science Engineering, DEIB, Politecnico di Milano
- Best Academic Team at the Twitter Challenge, 15th ACM Conference on Recommender Systems (RecSys 2021)
- Spotify Challenge, 2nd place Creative Track, 4th place Main Track, 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
Program Committee
- Conferences: SIGIR, CIKM, KDD, WWW, RecSys
- Journals: ACM TOIS, ACM TORS, ACM TSC, Future Generation Computer Systems, Neurocomputing, IEEE TETC, IEEE Transactions on Multimedia, Computers and Security, Information Processing & Management