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.
Supported by CAPES/Brazil and CNPq/Brazil.
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da Silva, A.R., Rodrigues, L.M., de Oliveira Rech, L., Luiz, A.F. (2019). RDFM: Resilient Distributed Factorization Machines. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2019. Lecture Notes in Computer Science(), vol 11509. Springer, Cham. https://doi.org/10.1007/978-3-030-20915-5_52
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DOI: https://doi.org/10.1007/978-3-030-20915-5_52
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