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PRACTICAL PROBLEMS AND SOLUTIONS TO THE USE OF THEORETICAL LAWS IN THE SCIENCES OF THE 2IST CENTURY
tashkent, o-8 MAv 2004 www.in~academy.uz
DEVELOPMENT OF INFORMATION MODELS FOR MAXIMUM VALUE CALCULATION PROCESSES IN SEARCH ENGINES
Saidova Fazilat Muzaffarovna
Teacher,University of Tashkent for Applied Sciences, Gavhar Street 1, Tashkent 100149, Uzbekistan sfazilat@bk.ru https://doi.org/10.5281/zenodo.13365715 Abstract: The rapid growth of online information and the increasing reliance on search engines have highlighted the need for effective methods to measure and maximize the value of search engine processes. This research focuses on the development of information models that facilitate the calculation of maximum value in search engine algorithms. The study begins by examining the current landscape of search engines and the challenges associated with maximizing value. It identifies key factors such as user satisfaction, information accessibility, trust, business success, user engagement, and innovation as crucial elements in defining value within search engine processes.To address these challenges, the research proposes the development of comprehensive information models that integrate various metrics and parameters to assess the value of search engine algorithms.
Keywords: Search engines, relevance metric, session analysis,maximum value calculation.
1 INTRODUCTION
Search engines have become an integral part of our daily lives, empowering us to access vast amounts of information with just a few keystrokes. The success of search engines lies in their ability to provide users with highly relevant and valuable search results. As the volume of online content continues to grow exponentially, the challenge of delivering maximum value to users becomes increasingly complex.Maximizing value in search engine algorithms is crucial for enhancing user experience, improving search accuracy, and increasing user satisfaction. When users enter a search query, they expect to receive the most relevant and valuable results that align with their
- F1 Score: Harmonic mean of precision and recall, providing a balance between the two metrics.
- Mean Average Precision (MAP): Average of precision values at various recall levels. Useful for evaluating ranked retrieval systems.
2. User Satisfaction Metrics:
- Click-Through Rate (CTR): Percentage of users who click on a search result after seeing it.
- Dwell Time: How much time users spend on a page after clicking on a search result. Longer dwell times typically indicate higher satisfaction.
- Bounce Rate: Percentage of users who leave a site without taking any further action after viewing a single page.
- User Surveys and Feedback: Direct feedback from users about the relevance and usefulness of search results.
3. Behavioral Analysis:
needs and preferences. Therefore, it is essential to develop robust and efficient information models that can accurately assess the value of search results and rank them accordingly.
2 METHODS OF SOLUTION
Evaluating the effectiveness of models and algorithms in enhancing search engine performance and user satisfaction involves several key metrics.
l.Relevance Metrics:
- Precision and Recall: Measure the ratio of relevant documents retrieved to the total retrieved (precision) and the ratio of relevant documents retrieved to the total number of relevant documents (recall).
- Session Analysis: Studying user behavior throughout a search session to understand satisfaction levels and the effectiveness of the search algorithm in guiding users to relevant information.
- Query Reformulation Rate: Percentage of users who refine their search queries, indicating dissatisfaction with initial results.
- Task Completion Rate: Percentage of users who successfully accomplish their search tasks.
4. Experimental Design:
- A/B Testing: Conducting experiments where users are randomly assigned to different versions of the search engine (e.g., with or without algorithmic enhancements) to measure the impact on performance and satisfaction.
- Multivariate Testing: Testing multiple variables simultaneously to understand their combined effects on performance and satisfaction.
5. Longitudinal Studies:
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- Long-term Usage Analysis: Tracking changes in user behavior and satisfaction over time after implementing algorithmic improvements.
- Cohort Analysis: Studying groups of users who started using the search engine at the same time to understand how algorithmic changes impact user retention and satisfaction.
6. Business Metrics:
- Revenue or Conversion Rate: If the search engine is part of an e-commerce platform, measuring the impact of algorithmic improvements on sales or conversions.
- Customer Retention: Analyzing whether algorithmic enhancements lead to higher customer retention rates.
By employing a combination of these metrics and methodologies, is comprehensively evaluated the effectiveness of developed models and algorithms in enhancing search engine performance and user satisfaction.
3 RESULTS AND SAMPLES
The literature on the development of information models for maximum value calculation processes in search engines reflects a diverse range of approaches and techniques aimed at improving search relevance and user satisfaction. Integration of machine learning, semantic search, user intent modeling, and advanced evaluation methodologies plays a pivotal role in advancing the effectiveness of search engine algorithms. Further research in this area is essential to address emerging challenges and enhance the search experience for users. These models incorporate user feedback, relevance metrics, content quality, credibility indicators, and business impact measurements. The research employs a multidisciplinary approach, drawing insights from information retrieval, data analytics, user experience, and business management. It leverages existing research and industry practices while introducing novel concepts to enhance the value calculation processes in search engines. The proposed information models are validated through extensive experimentation and analysis using large-scale datasets from diverse search engine environments. The evaluation considers both quantitative metrics and qualitative user feedback to ensure the accuracy and reliability of the models.
The findings demonstrate the effectiveness of the developed information models in capturing and evaluating the various dimensions of value in search engine algorithms. The models provide a comprehensive framework for search engine providers to optimize their algorithms, improve user satisfaction, and enhance the overall search experience.
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The research contributes to the field of search engine optimization by offering practical approaches and insights into maximizing value. The proposed information models can guide search engine providers in making data-driven decisions, prioritizing valuable content, and fostering innovation in the search engine industry. The outcomes of this research have implications for both researchers and practitioners seeking to enhance the value calculation processes in search engines.
4 SUMMARY
The development of information models for maximizing value calculation processes in search engines is a dynamic field of research focused on enhancing search relevance and user satisfaction. Existing literature explores a variety of approaches, including traditional Information Retrieval models, semantic search techniques, user intent modeling, and advanced evaluation methodologies. Key considerations include integrating machine learning for relevance assessment, understanding user intent, and incorporating user satisfaction metrics into value calculation processes. By leveraging these diverse methodologies, researchers aim to refine search engine algorithms to deliver more accurate, context-aware results and ultimately improve the overall search experience for users. Further research is needed to address evolving challenges and continue advancing the effectiveness of search engine models.
REFERENCES
[1] Паук - Описание системы. RASER Company. 1995.
[2] Kahle, B., and Medlar, A., "An Information System for Corporate Users: Wide Area Information Servers," Technical Report TMC-199, Thinking Machines, Inc., April 1991.
[3] Budi Yuwono, Dik L.Lee. Search and Ranking Algorithms for Locating Resources on the World Wide Web. In Proceedings of the Forth International Conference on the World Wide Web, New York, November, 1995.
[4] Koster, M., "ALIWEB: Archie-like Indexing in the Web," Computer Networks and ISDN Systems, 27(2), pp. 175-182, 1994.
[5] Martin Bartschi. An Overview of Information Retrieval Subjects. IEEE Computer, # 5, 1985, p. 67-84
[6] Дж. Солтон. Динамические библиотечно-информационные системы. Мир, Москва, 1979.
[7] Попов И.И. Оценка и оптимизация информационных систем. - М: МИФИ,. 1981.
[8] Решетников В.Н. Алгебраическая теория информационного поиска. Программирование, # 3, 1979, стр. 68-73.
PPSUTLSC-2024
PRACTICAL PROBLEMS AND SOLUTIONS TO THE USE OF THEORETICAL LAWS IN THE SCIENCES OF THE 2IST CENTURY
TASHKENT, 0-8 MAY 2004 WW W. ¡П "Э СЭС^ГПу . U Z
[9] Yu C.T., Salton G. Effective Information Retrieval Using Term Accuracy. Communication ACM, V.20, # 3, p. 135-142.
[10] T.Norault, M. McGill, and M.B. Koll. "A performance Evaluation of Similarity Measures, Document Term Weighing Schemes and Representations in Boolean Environment, Information Retrieval Search," R.N. Oddy et al., eds., Butterworth, London, 1981, p. 57-76.
[11] Yu C.T., Lam K., Salton G. "Term Weighting in Information Retrieval Using the Term Precession Model. Communication ACM, V.29, 1982, p. 152-170.
[12] И.И. Попов, П.Б. Храмцов. Распределение частоты встречаемости терминов для линейной модели информационного потока. НТИ, Сер.2, # 2, стр. 23-26, 1991.
[13] С.А. Айвазян, И.С. Енюков, Л.Д. Мешалкин. Прикладная статистика. Исследование Зависимостей. Москва, ФиС 1985.
[14] Васильев Ф. П. Методы оптимизации. — М.: Факториал Пресс, 2002. — 824 с.
[15] Груздева Т. В., Стрекаловский А. С. Локальный поиск в задачах с невыпуклыми ограничениями // Журн. вычисл. математики и мат. физики. — 2007. — Т. 47, № 3.
— C. 397-413., Стрекаловский А. С. 16.Элементы невыпуклой оптимизации. — Новосибирск: Наука, 2003.
— 352 с.
[16] Николаев А. И. Эффективный подход на основе машинного обучения для решения задачи о максимальной клике // Информационные технологии, Т. 22, № 4, 2016, С. 249-254. San Segundo P.,
[17] Nikolaev A., Batsyn M., Pardalos P.M. Improved Infra-Chromatic Bound for Exact Maximum Clique Search // Informatica. 2016. Vol. 27. No. 2. P. 463-487, San Segundo P., Lopez A., Batsyn M. V., Nikolaev A. I., Pardalos
[18] P. M. Improved initial vertex ordering for exact maximum clique search // Applied Intelligence. 2016. Vol. 45. No. 3. P. 868-880.
