Assigning discounts in a marketing campaign by using reinforcement learning and neural networks

This source preferred by Emili Balaguer-Ballester

Authors: Gómez-Pérez, G., Martín-Guerrero, J.D., Soria-Olivas, E., Balaguer-Ballester, E., Palomares, A. and Casariego, N.

Journal: Expert Systems With Applications

Volume: 36

Pages: 8022-8031

Publisher: Elsevier

This data was imported from DBLP:

Authors: Gómez-Pérez, G., Martín-Guerrero, J.D., Soria-Olivas, E., Balaguer-Ballester, E., Palomares, A. and Casariego, N.

Journal: Expert Syst. Appl.

Volume: 36

Pages: 8022-8031

This data was imported from Scopus:

Authors: Gómez-Pérez, G., Martín-Guerrero, J.D., Soria-Olivas, E., Balaguer-Ballester, E., Palomares, A. and Casariego, N.

Journal: Expert Systems with Applications

Volume: 36

Issue: 4

Pages: 8022-8031

ISSN: 0957-4174

DOI: 10.1016/j.eswa.2008.10.064

In this work, RL is used to find an optimal policy for a marketing campaign. Data show a complex characterization of state and action spaces. Two approaches are proposed to circumvent this problem. The first approach is based on the self-organizing map (SOM), which is used to aggregate states. The second approach uses a multilayer perceptron (MLP) to carry out a regression of the action-value function. The results indicate that both approaches can improve a targeted marketing campaign. Moreover, the SOM approach allows an intuitive interpretation of the results, and the MLP approach yields robust results with generalization capabilities. © 2008 Elsevier Ltd. All rights reserved.

This data was imported from Web of Science (Lite):

Authors: Gomez-Perez, G., Martin-Guerrero, J.D., Soria-Olivas, E., Balaguer-Ballester, E., Palomares, A. and Casariego, N.

Journal: EXPERT SYSTEMS WITH APPLICATIONS

Volume: 36

Issue: 4

Pages: 8022-8031

eISSN: 1873-6793

ISSN: 0957-4174

DOI: 10.1016/j.eswa.2008.10.064

The data on this page was last updated at 05:17 on May 25, 2020.