标题：Design of the architecture of an intelligent system for distributing commercial content in the internet space based on SEO-technologies, neural networks, and Machine Learning
期刊名称：Eastern-European Journal of Enterprise Technologies
印刷版ISSN：1729-3774
电子版ISSN：1729-4061
出版年度：2019
卷号：2
期号：2
页码：15-34
DOI：10.15587/1729-4061.2019.164441
语种：English
出版社：PC Technology Center
摘要：We have considered a task on designing an intelligent system of commercial distribution of informational products using a personalized approach to visitors based on the categories and tags of content that interests visitors. A general standard architecture of appropriate system has been developed using methods and personalization tools in the Internet environment with a core of automated recommendation of tags (categories) in the form of a neural network with controlled training. A personalized approach to the web site user results in a higher rate of sales. The system that was developed on the basis of modern SEO technologies considering the metrics for assessing the operation of an information and search module in the system makes it possible to select relevant content according to the user's personalized interests. The system has classes and subclasses that include real commercial informational products, interrelated by the built logical links, whose application promotes the intelligent supply of content based on the personalization of needs and interests of the user. In addition, based on modern methods of Machine Learning, the designed system learns to refine the results from searching the content in demand according to the personalized user's preferences. Personalization algorithms make it possible to associate each user with a list of products that are most likely to be of interest, and can predict what customers may want to see even if they are not aware of it yet. The aim of the intelligent system of e-commerce is to represent unique content based on the personalization approach and the use of tags. In addition to a standard text introduction of categories and tags based on images and product descriptions, the designed automation process defines tags and product categories. Recognition of context using deep neural networks now provides a technology for automated addition of tags to the description of goods at e-commerce web sites. The methods can be used to categorize facial expressions and recognize emotions.
