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  • Politecnico di Milano
  • Milano, Italy

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@recsyspolimi
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fernandobperezm/README.md

Hi there 👋

Welcome to my personal site. My socials are: LinkedIn, X/Twitter

Who am I?

I'm a Ph.D candidate at Politecnico di Milano where I study Recommender Systems (check out the lab's page), their application on industrial environments, online evaluation, experimentation of UI personalization, and more. Ultimately, I've been researching impression-aware recommender systems, where I focus on their characterization, evaluation, and experimentation. I'm interested on producing high quality research, applying what I learnt from working as a Software Engineer in the industry.

Research

The complete list of my publications can be seen on my Google Scholar profile. Here I list the three most relevant publications.

  • Impression-Aware Recommender Systems. Under revision. Fernando B. Pérez Maurera, Maurizio Ferrari Dacrema, Pablo Castells, Paolo Cremonesi. arXiv

  • ContentWise Impressions: An industrial dataset with impressions included. Accepted & presented at CIKM 2020. Fernando B. Pérez Maurera, Lorenzo Saule, Maurizio Ferrari Dacrema, Mario Scriminaci, Paolo Cremonesi. DOI: 10.1145/3340531.3412774, GitHub, arXiv, ACM DL

  • An Evaluation Study of Generative Adversarial Networks for Collaborative Filtering. Accepted & presented at ECIR 2022. Fernando B. Pérez Maurera, Maurizio Ferrari Dacrema, Paolo Cremonesi. DOI: 10.1007/978-3-030-99736-6_45, GitHub, arXiv, SpringerLink

How to reach me

For academic/research inquiries, send an email to my institutional email. For any other contribution, send an email to my personal address.

Pinned

  1. ContentWise/contentwise-impressions ContentWise/contentwise-impressions Public

    This repository contains the code used to run generate the data splits, run the hyperparameter tunings, and export the results presented in our article "ContentWise Impressions: An industrial datas…

    Python 12 2

  2. ecir-2022-an-evaluation-of-GAN-for-CF ecir-2022-an-evaluation-of-GAN-for-CF Public

    This repository contains the source code and data used in our experiments described in the paper "An evaluation of Generative Adversarial Networks for Collaborative Filtering". Refer to the README …

    Python 2

  3. recsyspolimi/recsys-2022-evaluation-of-recsys-with-impressions recsyspolimi/recsys-2022-evaluation-of-recsys-with-impressions Public

    Python