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Towards Feature Selection for Ranking and Classification Exploiting Quantum Annealers



Maurizio Ferrari Dacrema , Fabio Moroni , Riccardo Nembrini , Nicola Ferro , Guglielmo Faggioli , Paolo Cremonesi

* Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2022) 
* Source code is available on Github https://github.com/qcpolimi/SIGIR22_QuantumFeatureSelection.git 

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An Evaluation Study of Generative Adversarial Networks for Collaborative Filtering



Fernando BenjamĂ­n PĂ©rez Maurera , Maurizio Ferrari Dacrema , Paolo Cremonesi


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GAN-based Matrix Factorization for Recommender Systems



Ervin Dervishaj , Paolo Cremonesi

* Accepted at the 37th ACM/SIGAPP Symposium on Applied Computing (SAC '22) 

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Feature Selection for Recommender Systems with Quantum Computing



Riccardo Nembrini , Maurizio Ferrari Dacrema , Paolo Cremonesi

* Entropy 2021, 23(8), 970 

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Measuring the User Satisfaction in a Recommendation Interface with Multiple Carousels



Nicolò Felicioni , Maurizio Ferrari Dacrema , Paolo Cremonesi

* ACM International Conference on Interactive Media Experiences (IMX '21), June 21--23, 2021, Virtual Event, NY, USA 

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A Methodology for the Offline Evaluation of Recommender Systems in a User Interface with Multiple Carousels



Nicolò Felicioni , Maurizio Ferrari Dacrema , Paolo Cremonesi

* Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization (UMAP '21 Adjunct), June 21--25, 2021, Utrecht, Netherlands 

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On the instability of embeddings for recommender systems: the case of Matrix Factorization



Giovanni Gabbolini , Edoardo D'Amico , Cesare Bernardis , Paolo Cremonesi


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Critically Examining the Claimed Value of Convolutions over User-Item Embedding Maps for Recommender Systems



Maurizio Ferrari Dacrema , Federico Parroni , Paolo Cremonesi , Dietmar Jannach

* The 29th ACM International Conference on Information and Knowledge Management (CIKM '20), October 19--23, 2020, Virtual Event, Ireland 
* Source code available here: https://github.com/MaurizioFD/RecSys2019_DeepLearning_Evaluation 

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ContentWise Impressions: An industrial dataset with impressions included



Fernando BenjamĂ­n PĂ©rez Maurera , Maurizio Ferrari Dacrema , Lorenzo Saule , Mario Scriminaci , Paolo Cremonesi

* 8 pages, 2 figures 

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