Advertisement

LSTM-Based Consumption Type Prediction Model

  • Jinah Kim
  • Nammee MoonEmail author
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 536)

Abstract

In order to predict consumer behaviors, this paper proposes a consumption type prediction model using LSTM (Long Short Term Memory), a modification algorithm of RNN (Recurrent Neural Network). To do this, we derive and define age and gender consumption type patterns through Association Rule Analysis based on PrefixSpan algorithm using actual card consumption statistical data. Based on this, we used a pattern of daily consumption pattern as an input value, and constructed a model that predicts age and gender consumption patterns by learning the differences between the actual and forecast error rates in a week.

Keywords

LSTM (Long Short Term Memory) Deep learning PrefixSpan Association Rule Sequential pattern mining 

Notes

Acknowledgments

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government MSIP) (No. 2018020767).

References

  1. 1.
    Krebs, F., Lubascher, B., Moers, T., Schaap, P., Spanakis, G.: Social Emotion Mining Techniques for Facebook Posts Reaction Prediction, arXiv preprint arXiv:1712.03249 (2017)
  2. 2.
    Liu, G., et al.: Repeat buyer prediction for e-commerce. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM (2016)Google Scholar
  3. 3.
    Kim, K., Lee, J.-H.: Predictive models for customer churn using deep learning and boosted decision trees. J. Korean Inst. Intell. Syst. 28(1), 7–12 (2018)CrossRefGoogle Scholar
  4. 4.
    Sheil, H., Rana, O., Reilly, R.: Predicting purchasing intent: Automatic Feature Learning using Recurrent Neural Networks, arXiv preprint arXiv:1807.08207 (2018)
  5. 5.
    Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: International Conference on Advanced Information Systems Engineering, pp. 477–492. Springer, Cham (2017)Google Scholar
  6. 6.
    Nelson, D.M.Q, Pereira, A.C.M., de Oliveira, R.A.: Stock market’s price movement prediction with LSTM neural networks. In: 2017 International Joint Conference on Neural Networks (IJCNN). IEEE (2017)Google Scholar
  7. 7.
    Bang, S.-H., Bae, S.-H., Park, H.-K., Jeon, M.-J., Kim, J.-M., Park, Y.-T.: Approach for learning intention prediction model based on recurrent neural network. J. KIISE 45(4), 360–369 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Computer EngineeringHoseo UniversityAsan-siSouth Korea
  2. 2.Division of Computer and Information EngineeringHoseo UniversityAsan-siSouth Korea

Personalised recommendations