INVESTIGATION OF USAGE PROSPECTS AND PRINCIPLES OF MULTI-AGENT SEARCH ENGINE CONSTRUCTION

Authors

  • V.P. LYASHENKO
  • V.V. TERESHCHENKO

DOI:

https://doi.org/10.32782/2618-0340/2020.1-3.10

Keywords:

search engine optimization, search engine, search engine results, information search

Abstract

In conditions of the information society development, there is one of the most important tasks remains - to solve the problem of effective search and collection of the information. This is crucially important due to a growing diversity of information sources focused on developing different areas of human activities. Thus, there is a demand for new methods to ensure the effective information search. In this paper, the principles of functioning of information search systems and, in particular, of multiagent search engine was analyzed. Accordingly, a number of scientific works in the field of information search have been analyzed. During the analysis of the principles of the functioning of information search systems and the lot of scientific research in the field of information search, the prospect of using the distributed multiagent system in the framework of the improvement of search methods was established and the feasibility of using it to improve the accuracy of document evaluation was emphasized. The study established the prospect of using multiagency in the improvement of search methods, and in particular, in the construction of information search systems. The advantages of building a distributed multiagent search engine over centralized search systems were identified. It is also emphasized that multi-agent search can combine different approaches to solve the problem of search engine intellectualization and personalization. It was summarized that using the methodology of building a distributed multiagent system in the framework of improving search methods and, in particular, in the construction of information search systems, it is possible to ensure that the search engine first finds documents containing the necessary information. In addition, the basic principles of construction for the development of multiagent structure within the organization of information search were highlighted. The findings and suggestions of this study can be used in research and teaching. In particular, the results obtained from this study can be used to further analyze and refine information search methods.

References

Ашманов И. С., Иванов А. А. Продвижение сайта в поисковых системах. М.: Вильямс, 2016. 304 с.

Колисниченко Д. Н. Поисковые системы и продвижение сайтов. М.: Диалектика, 2014. 272 с.

Климчук С. О. Розроблення прецедентної системи підтримки прийняття рішень. Вісник Національного університету «Львівська Політехніка». 2010. № 689. С. 169–176.

Крохина О. И., Полосина М. Н. Первая книга SEO-копирайтера. Как написать текст для поисковых машин и пользователей. М.: Инфра-Инженерия, 2012. 216 с.

Маннинг К., Рагхаван П., Шютце Х. Введение в информационный поиск. М.: Вильямс, 2017. 640 с.

Терещенко В. В., Терещенко В. Л. Перспективність вдосконалення систем

інформаційного пошуку. ІТ-Перспектива: матеріали IV Всеукраїнської науково-практичної конференції. (м. Кременчук, 14-15 квітня 2017 р.). Кременчук: КрНУ, 2017. С. 26–28.

Терещенко В. В. Аналіз сучасних методів інформаційного пошуку. Вісник Кременчуцького національного університету імені Михайла Остроградського. 2018. Вип. 3 (110). С. 26–32.

Урвачева В. А. Обзор методов информационного поиска. Вестник Таганрогского института имени А.П. Чехова. 2016. №1. С. 457–463

Шокин Ю. И. Проблемы поиска информации. Новосибирск: Наука, 2010. 220 с.

Еремеев А. П., Варшавский П. Р. Моделирование рассуждений на основе прецедентов в интеллектуальных системах поддержки принятия решений.Искусственный интеллект и принятие решений. 2009. №2. С. 45-47.

Alexandros N., Mark M. Detecting Spam Web Pages through Content Analysis. Microsoft Research, 2012. РР. 1–6.

Brin S., Page L. The Anatomy of a Large-Scale Hypertextual Web Search Engine. Computer Networks and ISDN Systems. 2004. Vol. 30. PP. 107–117.

Ferber J. Les Systemes Multi-Agents. Vers une Intelligence Collective. Paris (France): InterEditions, 1995. 522 p.

Ganz A., Sieh L., Behavioral factors and SEO. Proceedings of the 24th International Conference on Computer Communications and Networks (ICCCN 2015). (Las Vegas, Nevada, USA August 3 – August 6, 2015). Scottsdale, Arizona, USA. РP. 218–223.

Mishne G., Carmel D. Blocking Blog Spam with Language Model Disagreement. Int’l Workshop Adversarial Information Retrieval on the Web (AIRWeb). 2005. PP. 955–969.

Published

2023-09-19