E-commerce products recognition based on a deep learning architecture: Theory and implementation

Abstract

Nowadays, the number of goods in the market is increasing rapidly. In order to realize the efficient circulation of e-business products, it is necessary to apply the state-of-the-art image recognition technologies to improve the service quality. In this paper, on the basis of the deep learning towards the characteristics of product image, we formulate a novel deep architecture for searching products in e-business context. Specifically, we have designed a product image recognition platform, which consists of various actual cases. We have analyzed the application of this platform. Meanwhile, this work also discusses the theoretical basis of deep learning technology in time series forecasting, and sums up the commodity sales forecasting as multivariable time series. Using the historical sales data of an e-commerce online store, we also formulate the mechanism of constructing the LSTM network model under the well-known Tensorflow framework. We evaluate our method by comparing it with diverse AR models, it can be concluded that LSTM network model is simple and with convenient data input. Besides, it can be shown that good marketing decisions and reasonable inventory management is significant in products purchasing.