Abstract
In the paper, different approaches to the problem of forecasting promotion efficiency are presented. For four defined indicators of promotion effect, prediction models using Gradient Boosting method and Deep Learning methods were trained. The comparison of the results is provided. The experiments were performed for three groups of products from a large grocery company.
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Acknowledgment
This work was partially supported by the European Union through the European Social Fund (grant POWR.03.05.00-00-Z305). The work was carried out in part within the project co-financed by European Funds entitled “Decision Support and Knowledge Management System for the Retail Trade Industry (SensAI)” (POIR.01.01.01-00-0871/17-00).
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Henzel, J., Sikora, M. (2020). Gradient Boosting and Deep Learning Models Approach to Forecasting Promotions Efficiency in FMCG Retail. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2020. Lecture Notes in Computer Science(), vol 12416. Springer, Cham. https://doi.org/10.1007/978-3-030-61534-5_30
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