Перегляд за Автор "Cherednichenko, O. Yu."

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Developing the Key Attributes for Product Matching Based onthe Item’s Image Tag Comparison
(CEUR Workshop Proceedings, 2020) Cherednichenko, O. Yu.; Yanholenko, O.; Kanishcheva, O.; Чередніченко, О. Ю.
With the constant growth of the number of products on e-marketplaces, buyers feel hard to find and choose items that would satisfy all their needs and expectations. Search and filtering algorithms of recommender systems, although are striving to help users, still fail quite often due to incomplete and inaccurate description of items. The given work suggests to combine analysis of bothitem description and item image in order to construct groups of similar items. Since a person can define whether two items are similar or not looking at two images and a brief description, it is suggested to form a set of similar items based on users’ judgments and then to extract the core of keywords for the specific type of prod-ucts. Further, it is proposed to use the given core to evaluate the similarity of any new itemaddedto the definite group. The case study deals with the building of the core of keywords for sneakers. The developed key attributes allow matching the items with a high precision, thus, proving the effectiveness of the method of the core construction.
Development of agent-oriented software components to retrieve the marketing information from the web
(Національний технічний університет «ХПІ», 2018) Cherednichenko, O. Yu.; Melnyk, K. V.; Kirkin, S. V.; Sokolov, D. V.; Matveiev, O. M.; Чередніченко, О. Ю.; Матвєєв, О. М.; Мельник, К. В.; Кіркін, С. В.; Соколов, Д. В.
The article is devoted to researching the processes of extracting marketing information from the Web space. Conclusions are drawn on the need tointroduce an information marketing system into modern business activities. A decision has been taken to develop software for the collection and analysis of marketing information. Identified and analyzed the main problems of collecting marketing information in the Web space. External systems for extracting and processing marketing information from the Web space were considered. During the analysis of the subject area, functional and non-functional requirements for the software being developed were formulated. Requirements for the selection of technologies for the development of an information system were defined. The analysis of software development technologies is carried out and the approach to the development of a software component is chosen. Such approaches to software development as: object-oriented programming, service-oriented architecture, component-oriented programming, agent-oriented programming were analyzed. A decision has been made to use the agent three-tier architecture in software development. The most commonly used programming languages in programming systems were: Java, KIF, KQML, AgentSpeak, April, TeleScript, Tcl/ Tk, Oz. Analyzed such popular agent platforms and their functions as: JADE, Cougaar, ZEUS, Jason. For the development of software, the JADE platform was chosen, its classes, methods and interfaces were examined. The advantages and peculiarities of the SOLID principle are analyzed. In detail, the levels of the CLEAN architecture are examined. And also explained the possibilities of software implementation of this architecture. A software architecture was developed for the data collection system. In accordance with the requirements, a selection of software development tools has been made. It was decided to use the programming language Java, Spring Framework, GoF design pattern, the template Dependency Injection, SOLID and CLEAN architectural principles. A software component was developed for marketing information gathering systems, which allows to optimize this process. The limitations and ways to improve the software system are analyzed.
Item Matching Based on Collection and Processing Customer Perceptionof Images
(CEUR Workshop Proceedings, 2020) Cherednichenko, O. Yu.; Vovk, M.; Ivashchenko, O.; Чередніченко, О. Ю.
The number of sellers and goods being sold on the e-marketplaces is growing, so the volume of data stored and processed by e-commerce information systems is increasing drastically. That is why the development of performance solutions is quite relevant. The given paper provides the approach of item match-ing based on the human perception of item images. The main goal of the study is to build a model for assessing the similarity of items. This paper provides a de-scription of a software product for comparing product images collected on online trading platforms. The user evaluates the product visually. The developed soft-ware implements the crowdsourcing data collecting based on the comparator identification method. The use of this method involves an experiment in which the user is offered two images, by comparing which the determined binary reac-tion is obtained. The results show the perspective of the mobile client application as part of an item matching system that aims to optimize the search for products on the Internet.
A Model for Estimating the Security Level of Mobile Applications: a Fuzzy Logic Approach
(CEUR Workshop Proceedings, 2020) Yanholenko, O.; Cherednichenko, O. Yu.; Yakovleva, O.; Arkatov, D.; Чередніченко, О. Ю.
In this paper a model for solving the problem of estimating the secu-rity level of mobile applications was proposed. The estimation is performed based on a fuzzy inference system of the Mamdani type. The input criteria were defined as the most important security threats by applying the Analytic hierar-chy process method. The pairwise comparison matrix was constructed from mobile security research on OWASP Top 10 Mobile Risks. The proposed methodology can be applied for any kind of mobile applications available for modern platforms, except specific cases when security analyst does not have a sufficient amount of information about the chosen application for performing the security level testing. Mobile security analysts can easily make further deci-sions about comprehensive mobile application security based on the results ob-tained with the help of the introduced model.
Multi-Agent Modeling of Project Management Processes in Distributed Teams
(CEUR Workshop Proceedings, 2021) Cherednichenko, O. Yu.; Matveiev, O. M.; Yanholenko, O.; Maneva, R.; Матвєєв, О. М.; Чередніченко, О. Ю.
Changes in the business environment, the innovative nature of projects, lack of necessary skills of project team members lead to increased uncertainty and inability to plan with a given degree of accuracy. Such projects use adaptive project and program management methodologies. In the field of information technology,the use of multi-agent systems is of particular interest. In the context of the use of multi-agent systems for the design of intelligent systems for various purposes, the development and study of a model and software implementation of a prototype of an agent platform are relevant. The aim of this work is to develop and research an agent platform that can be implemented in the work of the distributed team in order to improvetheassignment of tasks. The paper presents the formal agent architecture as a basis of multi-agent model. The task assignment is a case studyto implement and test multi-agent model prototype. The agent platform is developed based on Kotlin programming language. A prototype of the agent platform based on the FIPA specification allows to increase the productivity, scalability and interoperability of multi-agent system.
Towards Classifying HTML-embedded Product Data Based On Machine Learning Approach
(CEUR Workshop Proceedings, 2021) Matveiev, O. M.; Zubenko, A.; Yevtushenko, D.; Cherednichenko, O. Yu.; Матвєєв, О. М.; Чередніченко, О. Ю.
In this paper we explored machine learning approaches using descriptions and titles to classify footwear by brand. The provided data were taken from many different online stores. In particular, we have created a pipeline that automatically classifies product brands based on the provided data. The dataset is provided in JSON format and contains more than 40,000 rows. The categorization component was implemented using K-Nearest Neighbour (K-NN)and Support Vector Machine (SVM) algorithms. The results of the pipeline construction were evaluated basing on the classification report, especially the Precision weighted average value was considered during the calculation, which reached 79.0% for SVM and 72.0% for K-NN.
Towards Structuring of Electronic Marketplaces Contents: Items Normalization Technology
(CEUR Workshop Proceedings, 2020) Cherednichenko, O. Yu.; Yanholenko, O.; Vovk, M.; Sharonova, N.; Чередніченко, О. Ю.
The E-commerce industry is going strong and is bringing a great profit to its stakeholders. However, there is probably no buyer of the e-marketplace who has not faced the issues connected with inappropriate search results or inadequate filtering and recommendation of irrelevant products. Modern search and collab-orative filtering algorithms of e-commerce systems do work well with the input data of high quality but the reality is that often items’ description contains inac-curacies and incompleteness, which negatively affects the results. The given pa-per suggests the concept of e-marketplace items normalization which goal is to provide the unified and standardized patterns of items inside the system that can be used by search and filtering algorithms. Items normalization is implemented based on the algebra of predicates models specified in this work. The case study deals with constructing normalized models of knapsacks items from the online sports store. The developed models allowed to build 141 normalized item pat-terns with a unified set of attributes and their values.
Моделі формування рекомендацій у інтелектуальних системах електронної комерції
(Харківський національний університет Повітряних Сил імені Івана Кожедуба, 2020) Чередніченко, О. Ю.; Янголенко, О. В.; Іващенко, О. В.; Матвєєв, О. М.; Cherednichenko, O. Yu.; Yanholenko, O.; Ivashchenko, O.; Matveiev, O. M.
Результати роботи пошукових та фільтраційних механізмів сучасних систем електронної комерції не завжди задовольняють вимоги користувачів, що проявляється у неточних та неповних рекомендаціях товарів за пошуковим запитом. Удосконалення якості рекомендацій для покупців онлайн торгівельних платформ є актуальною задачею. Дана робота наводить моделі формування рекомендацій на основі методів кластерного аналізу, які дозволяють згрупувати схожі товари та схожих клієнтів за їхніми характеристиками. Наведені результати експерименту щодо формування рекомендацій придбання рюкзаків для покупців онлайн магазину спортивного обладнання.