6533b873fe1ef96bd12d5549
RESEARCH PRODUCT
Assigning discounts in a marketing campaign by using reinforcement learning and neural networks
Emilio Soria-olivasAlberto PalomaresJosé D. Martín-guerreroNicolás CasariegoGabriel Gómez-pérezEmili Balaguer-ballestersubject
Artificial neural networkComputer scienceGeneralizationbusiness.industrymedia_common.quotation_subjectAggregate (data warehouse)General EngineeringMachine learningcomputer.software_genreComputer Science ApplicationsFunction approximationArtificial IntelligenceMultilayer perceptronReinforcement learningState (computer science)Artificial intelligenceFunction (engineering)businesscomputermedia_commondescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2009-05-01 | Expert Systems with Applications |