Bayesian Factorization Machines for Risk Management and Robust Decision Making

Angulo Ardoy, Pablo and Gallego, Víctor and Gómez Ullate, David and Suárez García, Pablo (2018). Bayesian Factorization Machines for Risk Management and Robust Decision Making. In: "Mathematical and Statistical Methods for Actuarial Sciences and Finance". Springer, pp. 51-55. https://doi.org/10.1007/978-3-319-89824-7_9.

Description

Title: Bayesian Factorization Machines for Risk Management and Robust Decision Making
Author/s:
  • Angulo Ardoy, Pablo
  • Gallego, Víctor
  • Gómez Ullate, David
  • Suárez García, Pablo
Item Type: Book Section
Title of Book: Mathematical and Statistical Methods for Actuarial Sciences and Finance
Date: 2018
Subjects:
Faculty: E.T.S.I. Navales (UPM)
Department: Aeronaves y Vehículos Espaciales
Creative Commons Licenses: None

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Abstract

When considering different allocations of the marketing budget of a firm, some predictions, that correspond to scenarios similar to others observed in the past, can be made with more confidence than others, that correspond to more innovative strategies. Selecting a few relevant features of the predicted probability distribution leads to a multi-objective optimization problem, and the Pareto front contains the most interesting media plans. Using expected return and standard deviation we get the familiar two moment decision model, but other problem specific additional objectives can be incorporated. Factorization Machines, initially introduced for recommendation systems, but later used also for regression, are a good choice for incorporating interaction terms into the model, since they can effectively exploit the sparse nature of typical datasets found in econometrics.

More information

Item ID: 63581
DC Identifier: http://oa.upm.es/63581/
OAI Identifier: oai:oa.upm.es:63581
DOI: 10.1007/978-3-319-89824-7_9
Official URL: https://doi.org/10.1007/978-3-319-89824-7_9
Deposited by: Profesor Pablo Angulo
Deposited on: 04 Sep 2020 06:05
Last Modified: 04 Sep 2020 06:05
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