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ORCIDMaurizio Ferrari Dacrema
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2020 – today
- 2024
[j7]Riccardo Pellini
, Maurizio Ferrari Dacrema:
Analyzing the effectiveness of quantum annealing with meta-learning. Quantum Mach. Intell. 6(2): 48 (2024)- [c44]Andrea Pasin, Maurizio Ferrari Dacrema, Paolo Cremonesi, Nicola Ferro:
Overview of QuantumCLEF 2024: The Quantum Computing Challenge for Information Retrieval and Recommender Systems at CLEF. CLEF (2) 2024: 260-282 - [c43]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: 3032-3053 - [c42]Maurizio Ferrari Dacrema, Andrea Pasin, Paolo Cremonesi, Nicola Ferro:
Quantum Computing for Information Retrieval and Recommender Systems. ECIR (5) 2024: 358-362 - [c41]Andrea Pasin, Maurizio Ferrari Dacrema, Paolo Cremonesi, Nicola Ferro:
QuantumCLEF - Quantum Computing at CLEF. ECIR (5) 2024: 482-489 - [c40]Nicola Cecere, Andrea Pisani, Maurizio Ferrari Dacrema, Paolo Cremonesi:
Leveraging Semantic Embeddings of User Reviews with Off-the-Shelf LLMs for Recommender Systems. IIR 2024: 87-90 - [c39]Andrea Pisani, Nicola Cecere, Maurizio Ferrari Dacrema, Paolo Cremonesi:
Pre-Trained LLM Embeddings of Product Reviews for Recommendation. IIR 2024: 91-94 - [c38]Andrea Alari, Lorenzo Campana, Federico Giuseppe Ciliberto, Saverio Maggese, Carlo Sgaravatti, Francesco Zanella, Andrea Pisani, Maurizio Ferrari Dacrema:
Exploiting Contextual Normalizations and Article Endorsement for News Recommendation. RecSys Challenge 2024: 17-21 - [c37]Maurizio Ferrari Dacrema, Andrea Pasin, Paolo Cremonesi, Nicola Ferro:
Using and Evaluating Quantum Computing for Information Retrieval and Recommender Systems. SIGIR 2024: 3017-3020 - [i21]Nicolò Felicioni, Michael Benigni, Maurizio Ferrari Dacrema:
AutoOPE: Automated Off-Policy Estimator Selection. CoRR abs/2406.18022 (2024) - [i20]Riccardo Pellini, Maurizio Ferrari Dacrema:
Analyzing the Effectiveness of Quantum Annealing with Meta-Learning. CoRR abs/2408.00570 (2024) - [i19]Costantino Carugno, Maurizio Ferrari Dacrema, Paolo Cremonesi:
Adaptive Learning for Quantum Linear Regression. CoRR abs/2408.02833 (2024) - [i18]Simone Foderà, Gloria Turati, Riccardo Nembrini, Maurizio Ferrari Dacrema, Paolo Cremonesi:
Reinforcement Learning for Variational Quantum Circuits Design. CoRR abs/2409.05475 (2024) - 2023
- [j6]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): 19:1-19:8 (2023) - [c36]Andrea Pasin, Maurizio Ferrari Dacrema, Paolo Cremonesi, Nicola Ferro:
qCLEF: A Proposal to Evaluate Quantum Annealing for Information Retrieval and Recommender Systems. CLEF 2023: 97-108 - [c35]Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, Pablo Castells, Paolo Cremonesi:
Impressions in Recommender Systems: Present and Future. IIR 2023: 97-104 - [c34]Riccardo Pellini, Maurizio Ferrari Dacrema, Paolo Cremonesi:
Towards Improved QUBO Formulations of IR Tasks for Quantum Annealers. IIR 2023: 137-142 - [c33]Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, Pablo Castells, Paolo Cremonesi:
Characterizing Impression-Aware Recommender Systems. LERI@RecSys 2023: 22-33 - [c32]Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, Pablo Castells, Paolo Cremonesi:
Incorporating Impressions to Graph-Based Recommenders. LERI@RecSys 2023: 62-67 - [c31]Gloria Turati, Maurizio Ferrari Dacrema, Paolo Cremonesi:
Benchmarking Adaptative Variational Quantum Algorithms on QUBO Instances. QCE 2023: 407-413 - [c30]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: 33-38 - [c29]Maurizio Ferrari Dacrema, Pablo Castells, Justin Basilico, Paolo Cremonesi:
Workshop on Learning and Evaluating Recommendations with Impressions (LERI). RecSys 2023: 1248-1251 - [e1]Maurizio Ferrari Dacrema, Pablo Castells, Justin Basilico, Paolo Cremonesi:
Proceedings of the Workshop on Learning and Evaluating Recommendations with Impressions co-located with the 17th ACM Conference on Recommender Systems (RecSys 2023), Singapore, September 19th, 2023. CEUR Workshop Proceedings 3590, CEUR-WS.org 2023 [contents] - [i17]Gloria Turati, Maurizio Ferrari Dacrema, Paolo Cremonesi:
Benchmarking Adaptative Variational Quantum Algorithms on QUBO Instances. CoRR abs/2308.01789 (2023) - [i16]Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, Pablo Castells, Paolo Cremonesi:
Impression-Aware Recommender Systems. CoRR abs/2308.07857 (2023) - 2022
- [j5]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: 14779-14796 (2022) - [j4]Maurizio Ferrari Dacrema, Nicolò Felicioni, Paolo Cremonesi:
Offline Evaluation of Recommender Systems in a User Interface With Multiple Carousels. Frontiers Big Data 5: 910030 (2022) - [c28]Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, Paolo Cremonesi:
An Evaluation Study of Generative Adversarial Networks for Collaborative Filtering. ECIR (1) 2022: 671-685 - [c27]Maurizio Ferrari Dacrema, Nicolò Felicioni, Paolo Cremonesi:
Evaluating Recommendations in a User Interface With Multiple Carousels. IIR 2022 - [c26]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 - [c25]Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, Paolo Cremonesi:
Replication of Collaborative Filtering Generative Adversarial Networks on Recommender Systems. IIR 2022 - [c24]Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, Paolo Cremonesi:
Replication of Recommender Systems with Impressions. IIR 2022 - [c23]Nicolò Felicioni, Maurizio Ferrari Dacrema, Marcello Restelli, Paolo Cremonesi:
Off-Policy Evaluation with Deficient Support Using Side Information. NeurIPS 2022 - [c22]Pietro Chiavassa, Andrea Marchesin, Ignazio Pedone, Maurizio Ferrari Dacrema, Paolo Cremonesi:
Virtual Network Function Embedding with Quantum Annealing. QCE 2022: 282-291 - [c21]Gloria Turati, Maurizio Ferrari Dacrema, Paolo Cremonesi:
Feature Selection for Classification with QAOA. QCE 2022: 782-785 - [c20]Riccardo Nembrini, Costantino Carugno, Maurizio Ferrari Dacrema, Paolo Cremonesi:
Towards Recommender Systems with Community Detection and Quantum Computing. RecSys 2022: 579-585 - [c19]Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, Paolo Cremonesi:
Towards the Evaluation of Recommender Systems with Impressions. RecSys 2022: 610-615 - [c18]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: 2814-2824 - [r1]Maurizio Ferrari Dacrema, Iván Cantador, Ignacio Fernández-Tobías, Shlomo Berkovsky, Paolo Cremonesi:
Design and Evaluation of Cross-Domain Recommender Systems. Recommender Systems Handbook 2022: 485-516 - [d2]Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, Paolo Cremonesi:
An Evaluation of Generative Adversarial Networks for Collaborative Filtering - Supplemental Material. Version v1.0.1-ecir-2022-camera-ready-prep. Zenodo, 2022 [all versions]
- [d1]Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, Paolo Cremonesi:
An Evaluation Study of Generative Adversarial Networks for Collaborative Filtering - Supplemental Material. Version v1.1.1-ecir-2022-camera-ready. Zenodo, 2022 [all versions] - [i15]Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, Paolo Cremonesi:
An Evaluation Study of Generative Adversarial Networks for Collaborative Filtering. CoRR abs/2201.01815 (2022) - [i14]Maurizio Ferrari Dacrema, Fabio Moroni, Riccardo Nembrini, Nicola Ferro, Guglielmo Faggioli, Paolo Cremonesi:
Towards Feature Selection for Ranking and Classification Exploiting Quantum Annealers. CoRR abs/2205.04346 (2022) - [i13]Gloria Turati, Maurizio Ferrari Dacrema, Paolo Cremonesi:
Feature Selection for Classification with QAOA. CoRR abs/2211.02861 (2022) - 2021
- [j3]Riccardo Nembrini, Maurizio Ferrari Dacrema, Paolo Cremonesi:
Feature Selection for Recommender Systems with Quantum Computing. Entropy 23(8): 970 (2021) - [j2]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): 20:1-20:49 (2021) - [c17]Maurizio Ferrari Dacrema:
Demonstrating the Equivalence of List Based and Aggregate Metrics to Measure the Diversity of Recommendations (Student Abstract). AAAI 2021: 15779-15780 - [c16]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 - [c15]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: 28-33 - [c14]Maurizio Ferrari Dacrema, Nicolò Felicioni, Paolo Cremonesi:
Optimizing the Selection of Recommendation Carousels with Quantum Computing. RecSys 2021: 691-696 - [c13]Nicolò Felicioni, Maurizio Ferrari Dacrema, Paolo Cremonesi:
Measuring the User Satisfaction in a Recommendation Interface with Multiple Carousels. IMX 2021: 212-217 - [c12]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: 10-15 - [i12]Nicolò Felicioni, Maurizio Ferrari Dacrema, Paolo Cremonesi:
A Methodology for the Offline Evaluation of Recommender Systems in a User Interface with Multiple Carousels. CoRR abs/2105.06275 (2021) - [i11]Nicolò Felicioni, Maurizio Ferrari Dacrema, Paolo Cremonesi:
Measuring the User Satisfaction in a Recommendation Interface with Multiple Carousels. CoRR abs/2105.07062 (2021) - [i10]Riccardo Nembrini, Maurizio Ferrari Dacrema, Paolo Cremonesi:
Feature Selection for Recommender Systems with Quantum Computing. CoRR abs/2110.05089 (2021) - 2020
- [b1]Maurizio Ferrari Dacrema:
An assessment of reproducibility and methodological issues in neural recommender systems research. Polytechnic University of Milan, Italy, 2020 - [c11]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: 355-363 - [c10]Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, Lorenzo Saule, Mario Scriminaci, Paolo Cremonesi:
ContentWise Impressions: An Industrial Dataset with Impressions Included. CIKM 2020: 3093-3100 - [c9]Maurizio Ferrari Dacrema, Paolo Cremonesi, Dietmar Jannach:
Methodological Issues in Recommender Systems Research (Extended Abstract). IJCAI 2020: 4706-4710 - [c8]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: 29-33 - [i9]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 abs/2007.11893 (2020) - [i8]Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, Lorenzo Saule, Mario Scriminaci, Paolo Cremonesi:
ContentWise Impressions: An industrial dataset with impressions included. CoRR abs/2008.01212 (2020) - [i7]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 abs/2010.06233 (2020)
2010 – 2019
- 2019
- [j1]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): 291-343 (2019) - [c7]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: 7:1-7:5 - [c6]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: 72-76 - [c5]Maurizio Ferrari Dacrema, Paolo Cremonesi, Dietmar Jannach:
Are we really making much progress? A worrying analysis of recent neural recommendation approaches. RecSys 2019: 101-109 - [c4]Cesare Bernardis, Maurizio Ferrari Dacrema, Paolo Cremonesi:
Estimating Confidence of Individual User Predictions in Item-based Recommender Systems. UMAP 2019: 149-156 - [i6]Maurizio Ferrari Dacrema, Paolo Cremonesi, Dietmar Jannach:
Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches. CoRR abs/1907.06902 (2019) - [i5]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 abs/1908.11055 (2019) - [i4]Maurizio Ferrari Dacrema, Simone Boglio, Paolo Cremonesi, Dietmar Jannach:
A Troubling Analysis of Reproducibility and Progress in Recommender Systems Research. CoRR abs/1911.07698 (2019) - 2018
- [c3]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: 142-160 - [c2]Maurizio Ferrari Dacrema, Alberto Gasparin, Paolo Cremonesi:
Deriving Item Features Relevance from Collaborative Domain Knowledge. KaRS@RecSys 2018: 1-4 - [c1]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: 4:1-4:6 - [p1]Paolo Cremonesi, Franca Garzotto, Maurizio Ferrari Dacrema:
User Preference Sources: Explicit vs. Implicit Feedback. Collaborative Recommendations 2018: 233-252 - [i3]Cesare Bernardis, Maurizio Ferrari Dacrema, Paolo Cremonesi:
A novel graph-based model for hybrid recommendations in cold-start scenarios. CoRR abs/1808.10664 (2018) - [i2]Maurizio Ferrari Dacrema, Paolo Cremonesi:
Eigenvalue analogy for confidence estimation in item-based recommender systems. CoRR abs/1809.02052 (2018) - [i1]Maurizio Ferrari Dacrema, Alberto Gasparin, Paolo Cremonesi:
Deriving item features relevance from collaborative domain knowledge. CoRR abs/1811.01905 (2018)
Coauthor Index
[c44] [c43] [c42] [c41] [c40] [c39] [c37] [i19] [i18] [j6] [c36] [c35] [c34] [c33] [c32] [c31] [c29] [e1] [i17] [i16] [j5] [j4] [c28] [c27] [c26] [c25] [c24] [c23] [c22] [c21] [c20] [c19] [c18] [r1] [d2] [d1] [i15] [i14] [i13] [j3] [j2] [c16] [c14] [c13] [c12] [i12] [i11] [i10] [c11] [c10] [c9] [i9] [i8] [j1] [c6] [c5] [c4] [i6] [i5] [i4] [c2] [p1] [i3] [i2] [i1]
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