J
Jesús Bobadilla
Researcher at Technical University of Madrid
Publications - 71
Citations - 5500
Jesús Bobadilla is an academic researcher from Technical University of Madrid. The author has contributed to research in topics: Recommender system & Collaborative filtering. The author has an hindex of 23, co-authored 65 publications receiving 4565 citations. Previous affiliations of Jesús Bobadilla include Instituto Politécnico Nacional & Polytechnic University of Puerto Rico.
Papers
More filters
Journal ArticleDOI
Deep learning approach to obtain collaborative filtering neighborhoods
Jesús Bobadilla,Ángel González-Prieto,Ángel González-Prieto,Fernando Ortega,Raúl Lara-Cabrera +4 more
TL;DR: In this article, a deep learning architecture is proposed to efficiently and accurately obtain CF neighborhoods, which makes use of a classification neural network to encode the dataset patterns of the items, followed by a generative process that obtains the neighborhood of each item by means of an iterative gradient localization algorithm.
Deep Neural Aggregation for Recommending Items to Group of Users
TL;DR: In this paper , the authors analyze the current state of group recommender systems and propose two new models that use emerging deep learning architectures, and demonstrate the improvement achieved by employing the proposed models compared to the state-of-the-art models using four different datasets.
Posted Content
Deep Variational Models for Collaborative Filtering-based Recommender Systems
Jesús Bobadilla,Ángel González-Prieto,Fernando Ortega,Abraham Gutiérrez,Ángel González-Prieto +4 more
TL;DR: In this article, the authors apply the variational concept to inject stochasticity in the latent space of the deep architecture, introducing a variational technique in the neural collaborative filtering field.
Book ChapterDOI
Cooperative Visualization Framework Based on Video Streaming and Real-Time Vectorial Information
Jesús Bobadilla,Luis Mengual +1 more
TL;DR: The essential purpose of this paper is to describe a framework, in a simple and complete way, which enables the concurrent visualization of audio/video streaming combined with its corresponding synchronous cooperative vectorial information.