Maurizio Ferrari Dacrema

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 65

Top Topics

Methodological Issues

Recommender Systems

Top Venues

RecSys Challenge

Publications

Nicolò Felicioni, Michael Benigni, Maurizio Ferrari Dacrema
AutoOPE: Automated Off-Policy Estimator Selection. CoRR (2024)
Andrea Pasin, Maurizio Ferrari Dacrema, Paolo Cremonesi, Nicola Ferro
QuantumCLEF 2024: Overview of the Quantum Computing Challenge for Information Retrieval and Recommender Systems at CLEF. CLEF (Working Notes) (2024)
Andrea Pasin, Maurizio Ferrari Dacrema, Paolo Cremonesi, Nicola Ferro
QuantumCLEF - Quantum Computing at CLEF. ECIR (5) (2024)
Maurizio Ferrari Dacrema, Andrea Pasin, Paolo Cremonesi, Nicola Ferro
Quantum Computing for Information Retrieval and Recommender Systems. ECIR (5) (2024)
Maurizio Ferrari Dacrema, Andrea Pasin, Paolo Cremonesi, Nicola Ferro
Using and Evaluating Quantum Computing for Information Retrieval and Recommender Systems. SIGIR (2024)
Riccardo Pellini, Maurizio Ferrari Dacrema
Analyzing the effectiveness of quantum annealing with meta-learning. Quantum Mach. Intell. 6 (2) (2024)
Andrea Pasin, Maurizio Ferrari Dacrema, Paolo Cremonesi, Nicola Ferro
qCLEF: A Proposal to Evaluate Quantum Annealing for Information Retrieval and Recommender Systems. CLEF (2023)
Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, Pablo Castells, Paolo Cremonesi
Characterizing Impression-Aware Recommender Systems. LERI@RecSys (2023)
Maurizio Ferrari Dacrema, Pablo Castells, Justin Basilico, Paolo Cremonesi
Report on the Workshop on Learning and Evaluating Recommendations with Impressions (LERI) at RecSys 2023. SIGIR Forum 57 (2) (2023)
Gloria Turati, Maurizio Ferrari Dacrema, Paolo Cremonesi
Benchmarking Adaptative Variational Quantum Algorithms on QUBO Instances. CoRR (2023)
Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, Pablo Castells, Paolo Cremonesi
Incorporating Impressions to Graph-Based Recommenders. LERI@RecSys (2023)
Gloria Turati, Maurizio Ferrari Dacrema, Paolo Cremonesi
Benchmarking Adaptative Variational Quantum Algorithms on QUBO Instances. QCE (2023)
Paolo Basso, Arturo Benedetti, Nicola Cecere, Alessandro Maranelli, Salvatore Marragony, Samuele Peri, Andrea Riboni, Alessandro Verosimile, Davide Zanutto, Maurizio Ferrari Dacrema
Pessimistic Rescaling and Distribution Shift of Boosting Models for Impression-Aware Online Advertising Recommendation. RecSys Challenge (2023)
Riccardo Pellini, Maurizio Ferrari Dacrema, Paolo Cremonesi
Towards Improved QUBO Formulations of IR Tasks for Quantum Annealers. IIR (2023)
Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, Pablo Castells, Paolo Cremonesi
Impression-Aware Recommender Systems. CoRR (2023)
Maurizio Ferrari Dacrema, Pablo Castells, Justin Basilico, Paolo Cremonesi
Workshop on Learning and Evaluating Recommendations with Impressions (LERI). RecSys (2023)
Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, Pablo Castells, Paolo Cremonesi
Impressions in Recommender Systems: Present and Future. IIR (2023)
Gloria Turati, Maurizio Ferrari Dacrema, Paolo Cremonesi
Feature Selection for Classification with QAOA. QCE (2022)
Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, Paolo Cremonesi
An Evaluation Study of Generative Adversarial Networks for Collaborative Filtering. ECIR (1) (2022)
Maurizio Ferrari Dacrema, Nicolò Felicioni, Paolo Cremonesi
Evaluating Recommendations in a User Interface With Multiple Carousels. IIR (2022)
Pietro Chiavassa, Andrea Marchesin, Ignazio Pedone, Maurizio Ferrari Dacrema, Paolo Cremonesi
Virtual Network Function Embedding with Quantum Annealing. QCE (2022)
Maurizio Ferrari Dacrema, Fabio Moroni, Riccardo Nembrini, Nicola Ferro, Guglielmo Faggioli, Paolo Cremonesi
Towards Feature Selection for Ranking and Classification Exploiting Quantum Annealers. SIGIR (2022)
Maurizio Ferrari Dacrema, Fabio Moroni, Riccardo Nembrini, Nicola Ferro, Guglielmo Faggioli, Paolo Cremonesi
Feature Selection via Quantum Annealers for Ranking and Classification Tasks. IIR (2022)
Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, Paolo Cremonesi
Towards the Evaluation of Recommender Systems with Impressions. RecSys (2022)
Maurizio Ferrari Dacrema, Nicolò Felicioni, Paolo Cremonesi
Offline Evaluation of Recommender Systems in a User Interface With Multiple Carousels. Frontiers Big Data 5 (2022)
Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, Paolo Cremonesi
An Evaluation Study of Generative Adversarial Networks for Collaborative Filtering. CoRR (2022)
Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, Paolo Cremonesi
Replication of Recommender Systems with Impressions. IIR (2022)
Maurizio Ferrari Dacrema, Fabio Moroni, Riccardo Nembrini, Nicola Ferro, Guglielmo Faggioli, Paolo Cremonesi
Towards Feature Selection for Ranking and Classification Exploiting Quantum Annealers. CoRR (2022)
Gloria Turati, Maurizio Ferrari Dacrema, Paolo Cremonesi
Feature Selection for Classification with QAOA. CoRR (2022)
Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, Paolo Cremonesi
Replication of Collaborative Filtering Generative Adversarial Networks on Recommender Systems. IIR (2022)
Cesare Bernardis, Maurizio Ferrari Dacrema, Fernando Benjamín Pérez Maurera, Massimo Quadrana, Mario Scriminaci, Paolo Cremonesi
From Data Analysis to Intent-Based Recommendation: An Industrial Case Study in the Video Domain. IEEE Access 10 (2022)
Nicolò Felicioni, Maurizio Ferrari Dacrema, Marcello Restelli, Paolo Cremonesi
Off-Policy Evaluation with Deficient Support Using Side Information. NeurIPS (2022)
Riccardo Nembrini, Costantino Carugno, Maurizio Ferrari Dacrema, Paolo Cremonesi
Towards Recommender Systems with Community Detection and Quantum Computing. RecSys (2022)
Riccardo Nembrini, Maurizio Ferrari Dacrema, Paolo Cremonesi
Feature Selection for Recommender Systems with Quantum Computing. Entropy 23 (8) (2021)
Nicolò Felicioni, Maurizio Ferrari Dacrema, Fernando Benjamín Pérez Maurera, Paolo Cremonesi
Measuring the Ranking Quality of Recommendations in a Two-Dimensional Carousel Setting. IIR (2021)
Nicolò Felicioni, Maurizio Ferrari Dacrema, Paolo Cremonesi
A Methodology for the Offline Evaluation of Recommender Systems in a User Interface with Multiple Carousels. CoRR (2021)
Nicolò Felicioni, Maurizio Ferrari Dacrema, Paolo Cremonesi
A Methodology for the Offline Evaluation of Recommender Systems in a User Interface with Multiple Carousels. UMAP (Adjunct Publication) (2021)
Riccardo Nembrini, Maurizio Ferrari Dacrema, Paolo Cremonesi
Feature Selection for Recommender Systems with Quantum Computing. CoRR (2021)
Nicolò Felicioni, Maurizio Ferrari Dacrema, Paolo Cremonesi
Measuring the User Satisfaction in a Recommendation Interface with Multiple Carousels. IMX (2021)
Nicolò Felicioni, Maurizio Ferrari Dacrema, Paolo Cremonesi
Measuring the User Satisfaction in a Recommendation Interface with Multiple Carousels. CoRR (2021)
Maurizio Ferrari Dacrema, Simone Boglio, Paolo Cremonesi, Dietmar Jannach
A Troubling Analysis of Reproducibility and Progress in Recommender Systems Research. ACM Trans. Inf. Syst. 39 (2) (2021)
Luca Carminati, Giacomo Lodigiani, Pietro Maldini, Samuele Meta, Stiven Metaj, Arcangelo Pisa, Alessandro Sanvito, Mattia Surricchio, Fernando Benjamín Pérez Maurera, Cesare Bernardis, Maurizio Ferrari Dacrema
Lightweight and Scalable Model for Tweet Engagements Predictions in a Resource-constrained Environment. RecSys Challenge (2021)
Maurizio Ferrari Dacrema
Demonstrating the Equivalence of List Based and Aggregate Metrics to Measure the Diversity of Recommendations (Student Abstract). AAAI (2021)
Maurizio Ferrari Dacrema, Nicolò Felicioni, Paolo Cremonesi
Optimizing the Selection of Recommendation Carousels with Quantum Computing. RecSys (2021)
Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, Lorenzo Saule, Mario Scriminaci, Paolo Cremonesi
ContentWise Impressions: An industrial dataset with impressions included. CoRR (2020)
Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, Lorenzo Saule, Mario Scriminaci, Paolo Cremonesi
ContentWise Impressions: An Industrial Dataset with Impressions Included. CIKM (2020)
Nicolò Felicioni, Andrea Donati, Luca Conterio, Luca Bartoccioni, Davide Yi Xian Hu, Cesare Bernardis, Maurizio Ferrari Dacrema
Multi-Objective Blended Ensemble For Highly Imbalanced Sequence Aware Tweet Engagement Prediction. RecSys Challenge (2020)
Maurizio Ferrari Dacrema, Federico Parroni, Paolo Cremonesi, Dietmar Jannach
Critically Examining the Claimed Value of Convolutions over User-Item Embedding Maps for Recommender Systems. CoRR (2020)
Sebastiano Antenucci, Simone Boglio, Emanuele Chioso, Ervin Dervishaj, Shuwen Kang, Tommaso Scarlatti, Maurizio Ferrari Dacrema
Artist-driven layering and user's behaviour impact on recommendations in a playlist continuation scenario. CoRR (2020)
Maurizio Ferrari Dacrema, Paolo Cremonesi, Dietmar Jannach
Methodological Issues in Recommender Systems Research (Extended Abstract). IJCAI (2020)
Maurizio Ferrari Dacrema, Federico Parroni, Paolo Cremonesi, Dietmar Jannach
Critically Examining the Claimed Value of Convolutions over User-Item Embedding Maps for Recommender Systems. CIKM (2020)
Luca Luciano Costanzo, Yashar Deldjoo, Maurizio Ferrari Dacrema, Markus Schedl, Paolo Cremonesi
Towards Evaluating User Profiling Methods Based on Explicit Ratings on Item Features. IntRS@RecSys (2019)
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. User Model. User Adapt. Interact. 29 (2) (2019)
Maurizio Ferrari Dacrema, Simone Boglio, Paolo Cremonesi, Dietmar Jannach
A Troubling Analysis of Reproducibility and Progress in Recommender Systems Research. CoRR (2019)
Maurizio Ferrari Dacrema, Paolo Cremonesi, Dietmar Jannach
Are we really making much progress? A worrying analysis of recent neural recommendation approaches. RecSys (2019)
Edoardo D'Amico, Giovanni Gabbolini, Daniele Montesi, Matteo Moreschini, Federico Parroni, Federico Piccinini, Alberto Rossettini, Alessio Russo Introito, Cesare Bernardis, Maurizio Ferrari Dacrema
Leveraging laziness, browsing-pattern aware stacked models for sequential accommodation learning to rank. RecSys Challenge (2019)
Luca Luciano Costanzo, Yashar Deldjoo, Maurizio Ferrari Dacrema, Markus Schedl, Paolo Cremonesi
Towards Evaluating User Profiling Methods Based on Explicit Ratings on Item Features. CoRR (2019)
Maurizio Ferrari Dacrema, Paolo Cremonesi, Dietmar Jannach
Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches. CoRR (2019)
Cesare Bernardis, Maurizio Ferrari Dacrema, Paolo Cremonesi
Estimating Confidence of Individual User Predictions in Item-based Recommender Systems. UMAP (2019)
Cesare Bernardis, Maurizio Ferrari Dacrema, Paolo Cremonesi
A novel graph-based model for hybrid recommendations in cold-start scenarios. CoRR (2018)
Maurizio Ferrari Dacrema, Alberto Gasparin, Paolo Cremonesi
Deriving item features relevance from collaborative domain knowledge. CoRR (2018)
Maurizio Ferrari Dacrema, Paolo Cremonesi
Eigenvalue analogy for confidence estimation in item-based recommender systems. CoRR (2018)
Francesco Amigoni, Maurizio Ferrari Dacrema, Alessandro Donati, Christian Laroque, Michèle Lavagna, Alessandro Riva
Aggregating Models for Anomaly Detection in Space Systems: Results from the FCTMAS Study. IAS (2018)
Maurizio Ferrari Dacrema, Alberto Gasparin, Paolo Cremonesi
Deriving Item Features Relevance from Collaborative Domain Knowledge. KaRS@RecSys (2018)
Sebastiano Antenucci, Simone Boglio, Emanuele Chioso, Ervin Dervishaj, Shuwen Kang, Tommaso Scarlatti, Maurizio Ferrari Dacrema
Artist-driven layering and user's behaviour impact on recommendations in a playlist continuation scenario. RecSys Challenge (2018)