Study on convolutional neural network and its application in data mining and sales forecasting for E-commerce

Hong Pan and Hanxun Zhou ()
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Hong Pan: Liaoning University
Hanxun Zhou: Liaoning University

Electronic Commerce Research, 2020, vol. 20, issue 2, No 5, 297-320

Abstract: Abstract In recent years, the rapid development of e-commerce has brought great convenience to people. Compared with traditional business environment, e-commerce is more dynamic and complex, which brings many challenges. Data mining technology can help people better deal with these challenges. Traditional data mining technology cannot effectively use the massive data in the electricity supplier, it relies on the time-consuming and labour-consuming characteristic engineering, and the obtained model is not scalable. Convolutional neural network can effectively use a large amount of data, and can automatically extract effective features from the original data, with higher availability. In this paper, convolutional neural network is used to mine e-commerce data to achieve the prediction of commodity sales. First, this article combines the inherent nature of the relevant merchandise information with the original cargo log data that can be converted into a specific “data frame” format. Raw log data includes items sold over a long period of time, price, quantity view, browse, search, search, times collected, number of items added to cart, and many other metrics. Then, convolutional neural network is applied to extract effective features on the data frame. Finally, the final layer of the convolutional neural network uses these features to predict sales of goods. This method can automatically extract effective features from the original structured time series data by convolutional neural network, and further use these features to achieve sales forecast. The validity of the proposed algorithm is verified on the real e-commerce data set.

Keywords: Convolutional neural network; E-commerce; Data mining; Sales forecasting (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (7)

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DOI: 10.1007/s10660-020-09409-0

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