LSTM-Based Consumption Type Prediction Model
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.
KeywordsLSTM (Long Short Term Memory) Deep learning PrefixSpan Association Rule Sequential pattern mining
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government MSIP) (No. 2018020767).
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