The subject of research in the article is machine learning models for classifying web-pages by quality and compliance with SEO rules. The goal of the article is improving the efficiency of search engines by establishing and using factors that have the greatest impact on the degree of SEO optimization of web pages. The article solves the following tasks: study of the effectiveness of using machine learning methods to build a classification model that automatically classifies web pages according to the degree of adaptation to SEO optimization recommendations; assessment of the influence of relevant page factors (text on a web page, text in meta tags, links, image, HTML code) on the degree of SEO optimization using the developed classification models. The following methods are used: machine learning methods, classification methods and statistical methods. The following results were obtained: analysis of the effectiveness of the application of machine learning methods to determine the degree of adaptation of a web page to SEO recommendations was carried out; classifiers were trained on a data set of web pages randomly selected from the DMOZ catalog and rated by three independent SEO experts in the categories: “low SEO”, “medium SEO” and “high SEO”; five main classifiers were tested (decision trees, naive Bayes, logistic regression, KNN and SVM), on the basis of which it was revealed that all the studied models received greater accuracy (from 54.69% to 69.67%) than the accuracy of the baseline (48.83%); the results of the experiments confirm the hypothesis about the effectiveness of adapting web pages to SEO recommendations using classification algorithms based on machine learning. Conclusions. It was confirmed that with the help of classification algorithms built on the basis of machine learning and the knowledge of experts, it is possible to effectively adjust web pages to SEO recommendations. The considered methods can be adapted for various search engines and applicable to different languages, provided that a stamping or lemmatization algorithm has been developed for them. The results of the study can be used in the development of automated software to support the work of SEO in audit technologies to identify web pages in need of optimization and in spam detection processes.
The subject of research in the article is the topological structures of closed-loop logistics networks. The goal of the article is to increase the efficiency of centralized logistics networks by developing a mathematical model for a two-criteria problem of optimizing topological structures in the process of their reengineering. The article solves the following tasks: analysis of the current state of the problem of structural and topological optimization of logistics networks; formalization of the problem of optimization of logistics networks as geographically distributed objects; synthesis of objective functions of the mathematical model of a two-criterion optimization problem for centralized three-level topological structures of closed logistics networks at the reengineering stage; development of a system of constraints of the mathematical model of the problem of optimizing centralized three-level topological structures of closed logistics networks; a function for evaluating the overall utility of options based on the Kolmogorov-Gabor polynomial is offered. The following methods are used: methods of systems theory, methods of utility theory, optimization and operations research. The following results were obtained: the analysis of the current state of the problem of system optimization of logistics networks, mathematical models and methods for its solution was carried out; formalization of the problem of structural and topological optimization of logistics networks as geographically distributed objects; a mathematical model of a two-criterion task of reengineering of three-level topological structures of logistics networks in terms of costs and efficiency with integrated points of production and processing has been developed (originality). Conclusions: Based on the results of the analysis of the problem of optimizing the topological structures of logistics systems, it has been established that the problems of direct and reverse logistics are still considered as conditionally independent, which does not allow obtaining effective global solutions. In the context of expanding the network of consumers, changes in delivery volumes, the introduction of environmental restrictions, it is proposed to reengineer the networks, which provides for their radical redesign. The formulated statement and the developed mathematical model of a two-criterion (in terms of cost and efficiency) optimization problem for three-level topological structures for combined production and processing points will increase the efficiency of logistics networks with reverse flows by reducing the cost of reengineering (practical value).
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