The development of commercial activity by means of information systems provides many advantages for consumers of services and for companies that make such proposals. Exploring new markets for goods and services by internet companies provides great opportunities to consumers for analyzing and buying new products. The rapid development of computer technologies and information systems allows customers to buy the product even on the phone. However, the part of retail sales using such systems is still very small. A traditional e-commerce model based on finding a product or service on the Internet is becoming a bottleneck, impeding further company development. Instead, it can be replaced by personalized systems that give many benefits.
The problem of big data analytics in personalized systems of electronic commerce has been considered. An overview of existing methods of machine learning (Random Forest, AdaBoost, Multilayer Perceptron, SGTM neural-like structure, Linear Regression using Gradient Descent, General Regression Neural Network, Regressor Based on the Support Vectors Machine) for solving regression problems in such systems has been made. The advantages and disadvantages of the considered methods and algorithms have been presented. The methods of machine learning have been used for forecasting the number of consumer expenses of the retail store. The accuracy of the forecasting and the time of training procedures of all considered methods has been experimentally determined. Despite the high temporal characteristics of the work, methods based on the Support Vectors Machine and the Stochastic Gradient Descent do not provide sufficient precision. The efficiency of neural network methods is also not satisfactory in terms of accuracy and training time. It has been established that the ensemble methods provide the highest accuracy with satisfactory time characteristics of the training procedures for a given problem.
Keywords: ensemble, e-commerce, machine learning, forecasting, random forest, regression task, artificial intelligence.
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