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Name: Fernando Benjamín PÉREZ MAURERA

Type: User

Company: Politecnico di Milano

Bio: Ph.D. in Information Technology | Computer Engineer | Artificial Intelligence | Machine Learning | Recommender Systems

Twitter: FernandoBPerezM

Location: Madrid, Spain

Hello there 👋

Welcome to my personal site. I'm Fernando B. Pérez Maurera, a scientist and engineer experienced in the development and deployment of Artificial Intelligence, Machine Learning, and Software Engineering products.

Work Experience

In the past five years, I've been a ML and Recommender Systems Scientist at Politecnico di Milano, where I mainly studied Recommender Systems and their development and application on industrial environments. My works focused on designing and experimenting with "Impression-Aware Recommender Systems", a new family of recommenders that leverage impressions (past recommendations) and interactions (users' actions) to produce relevant recommendations.

Prior to becoming a scientist, I was a Software Engineer at Mahisoft, an IT consulting company. As a Software Engineer, I worked on multiple agile teams in the entire development stack, capturing requirements and transforming them into production-grade large-scale systems. As a team, we used the industry-standard yet well-tested technologies. We developed frontends using React.js and typescript and backends using .NET alongside SQL Server, or Spring Boot alongside MySQL.

Academic Titles

Ph.D. in Information Technology from Politecnico di Milano. December 2019 - May 2024.
Cum Laude Computer Engineer from Universidad Simón Bolívar. September 2012 - December 2018.

Scientific Publications

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 repo, 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 repo, arXiv, SpringerLink

How to reach me

For academic/research or personal inquiries, please send an email to my personal address. You may also contact me through other social networks; my handles are fernandobperezm on LinkedIn and @fernandobperezm on X/Twitter.

Fernando Benjamín PÉREZ MAURERA's Projects

2020-data-mining-and-text-mining

4elementstodie

android

Todo lo relacionado con mis proyectos android.

basesdedatosusb

Databases I

blog

Project that holds my own implementation of the "Building web applications with Spring Boot and Kotlin" tutorial

data-and-results-visualization

ecir-2022-an-evaluation-of-gan-for-cf

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 file to run our experiments.

fernandobperezm

hackathon

Game made in Unity for Corriere Hackaton 2016 at Milano. 36 hours for developing a game.

parsifal

Parsifal is a tool to assist researchers to perform Systematic Literature Reviews

polimi-design-implementation-mobile-applications

polimi-dima-lifemanager

Life Manager is an app that handles for you your favorite services such as email, social networks, music based on your current location. Evaluation for the course Design and Implementation of Mobile Applications held at Politecnico di Milano under supervision of Prof. Luciano Baresi

polimi-recommender-systems

polimi-videogames-design-implementation

python

All Algorithms implemented in Python

recsys-cotraining

This repository holds the implementation, for the thesis project: Improving Collaborative Filtering Techniques by the use of Co-Training in Recommender Systems. This project was done under a thesis research for Fernando Benjamín Pérez Maurera, under supervision of Professor Paolo Cremonesi and Engineer Maurizio Ferrari, at Politecnico di Milano.