Optimización del filtrado colaborativo basado en factorización matricial mediante la relevancia de las preferencias de los usuarios

Title Translation: Optimization of collaborative filtering based on matrix factorization through relevance of user preferences

Collaborative filtering based on matrix factorization has become the reference method for the recommendation of products or services due to the high precision of recommendations it generates. The experimental results carried out on the datasets of MovieLens 100K, MovieLens lM and Netflix demonstrate a clear improvement in terms of quality of predictions and recommendations compared to other matrix factorization techniques.

https://search.proquest.com/docview/2388304715

About Jesús Mayor

Jesús Mayor is since 2019 a full-time lecturer and researcher in Politécnica de Madrid University. He received MS degree in Computer Science (CEU San Pablo, 2013), MS degree in Computer Graphics (U-tad, 2014) and PhD degree in Computer Science (Rey Juan Carlos University, 2020) in Madrid. His studies are focused on computer graphics and data science

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