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2019 | OriginalPaper | Chapter

RDFM: Resilient Distributed Factorization Machines

Authors : André Rodrigo da Silva, Leonardo M. Rodrigues, Luciana de Oliveira Rech, Aldelir Fernando Luiz

Published in: Artificial Intelligence and Soft Computing

Publisher: Springer International Publishing

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Abstract

Factorization Machines algorithms have been successfully applied to recommender systems due to their ability to handle data sparsity and the cold-start problem. Their scalability makes it suitable to produce evergrowing complex predictive models, which are based on Big Data without performance degradation. The algorithm has been scaled to contexts of distributed and parallel computation, but in general with the strong assumption that those environments are safe and are not subject to arbitrary errors, malicious attacks, and hardware failures. In this work, we show that a distributed average consensus strategy is capable to deal with unsafe and dynamic learning environments.

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previous chapter Affective Context-Aware Systems: Architecture of a Dynamic Framework
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Metadata
Title
RDFM: Resilient Distributed Factorization Machines
Authors
André Rodrigo da Silva
Leonardo M. Rodrigues
Luciana de Oliveira Rech
Aldelir Fernando Luiz
Copyright Year
2019
Book
Artificial Intelligence and Soft Computing

Print ISBN: 978-3-030-20914-8

Electronic ISBN: 978-3-030-20915-5

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-20915-5_52

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