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

SIGIR 2024! Our proposed tutorial “Using and Evaluating Quantum Computing for Information Retrieval and Recommender Systems” has been accepted at SIGIR 2024!

QuantumCLEF! Check out our QuantumCLEF lab! We provide access to a real quantum annealer (D-Wave) to participants, the goal is to develop and test feature selection and clustering algorithms for Recommender Systems and Information Retrieval tasks!

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 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

Program Committee

• Conferences: RecSys, SIGIR, KDD, CIKM, WWW, WSDM, UMAP
• Journals: Quantum Information Processing (QIP), ACM Transactions on Information Systems (TOIS), ACM Transactions on Recommender Systems (TORS), Future Generation Computer Systems, Neurocomputing, IEEE Transactions on Multimedia, Computers and Security, Information Processing & Management