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• W2951927636 abstract "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." @default.
• W2951927636 created "2019-06-27" @default.
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• W2951927636 date "2019-05-23" @default.
• W2951927636 modified "2023-09-27" @default.
• W2951927636 title "Predicting Sparse Clients' Actions with CPOPT-Net in the Banking Environment." @default.
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• W2951927636 hasPublicationYear "2019" @default.
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