Text2Price: Deep Learning for Price Prediction

Abstract

There are many methods and strategies that can be used when determining the selling price of a product in the online marketplace. Correct pricing of a product is an important factor affecting the overall success and profitability of the e-commerce business. Considering all these issues, the need to develop tools that will help the seller in the process of deciding the price of a product arises. In this paper, we designed a model that predicts the price of a product using its title, supplier, category and description information. Our technique is based on using only a single text data for price estimation. For this purpose, we concatenate product information in a string while preserving their attribute information. The task of preprocessing various feature types becomes simple and quick using this method. The main contribution of our approach is designing a model that is applicable for various prediction tasks without task-oriented implementation. To build the prediction model, we used deep learning methods which are based on RNN and CNN and we compared their performances. According to the results, LSTM-based models have achieved more accurate predictions with 6.1646 mean absolute percentage error (MAPE). Also, CNN-based models had 3x times faster running time advantage while having a minor increase in MAPE with 7.1387 compared to LSTM-based models.

Keywords

• price prediction
• deep learning
• LSTM
• CNN

References

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