Reference : Predicting Sparse Clients' Actions with CPOPT-Net in the Banking Environment
Scientific congresses, symposiums and conference proceedings : Paper published in a journal
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/40107
Predicting Sparse Clients' Actions with CPOPT-Net in the Banking Environment
English
Charlier, Jérémy Henri J. mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
State, Radu mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Hilger, Jean mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
May-2019
32nd Canadian Conference on Artificial Intelligence Proceedings
Yes
International
32nd Canadian Conference on Artificial Intelligence
from 28-05-2019 to 31-05-2019
[en] Tensor Decomposition ; Personalized Recommendation ; Neural Networks
[en] The digital revolution of the banking system with evolving European regulations have pushed the major banking actors to innovate by a newly use of their clients' digital information. Given highly sparse client activities, we propose CPOPT-Net, an algorithm that combines the CP canonical tensor decomposition, a multidimensional matrix decomposition that factorizes a tensor as the sum of rank-one tensors, and neural networks. CPOPT-Net removes efficiently sparse information with a gradient-based resolution while relying on neural networks for time series predictions. Our experiments show that CPOPT-Net is capable to perform accurate predictions of the clients' actions in the context of personalized recommendation. CPOPT-Net is the first algorithm to use non-linear conjugate gradient tensor resolution with neural networks to propose predictions of financial activities on a public data set.
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Services and Data management research group (SEDAN)
Researchers ; Professionals ; Students ; General public
http://hdl.handle.net/10993/40107

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