Discipline of the basic profile of the professional cycle. Students will receive basic web search algorithms, create their own web crawler, and evaluate the quality of the collected results. To master the discipline, students need knowledge gained as a result of studying the disciplines "Machine Learning", "Probability Theory and Mathematical Statistics".
Learning Objectives
Formation of students' theoretical knowledge and practical skills of web search and data ranking.
Expected Learning Outcomes
Knows the technology of evaluation of search quality.
Able to collect data from web resources.
Has skills of using direct ranking methods and methods of ranking using machine learning.
Course Contents
Section 1. Assessment of the quality of information retrieval
Section 2. Preparation of data for search, request processing
Section 3. Classical approaches to ranging, application of semantic methods and machine learning
Section 4. Federated search, click models
Assessment Elements
Exam №2
The oral exam is carried out in the form of answers to the questions of the exam ticket. The exam ticket contains two questions from the list of questions for the exam. 2.5 hours are given to prepare the answer.
Course project №2
As a course project, students will be required to implement a project that uses modern web search and ranking methods to solve applied task. Project completion time is 5 weeks.
Exam №1
The oral exam is carried out in the form of answers to the questions of the exam ticket. The exam ticket contains two questions from the list of questions for the exam. 2.5 hours are given to prepare the answer.
Course project №1
As a course project, students will be required to implement a project that uses modern web search and ranking methods to solve applied task. Project completion time is 5 weeks.
Interim Assessment
2025/2026 1st module
0.5 * Exam №1 + 0.5 * Course project №1
2025/2026 2nd module
0.5 * Course project №2 + 0.5 * Exam №2
Bibliography
Recommended Core Bibliography
Gossen, T. (2015). Search Engines for Children : Search User Interfaces and Information-Seeking Behaviour. Wiesbaden: Springer Vieweg. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1159664
Sándor Dominich. The Modern Algebra of Information Retrieval (2008), Springer
Recommended Additional Bibliography
Advances in information retrieval: 36th European Conference on IR Research, ECIR 2014, Amsterdam, The Netherlands, April 13-16, 2014: proceedings. (2014). Springer. https://doi.org/10.1007/978-3-319-06028-6
Hwee Tou Ng, Mun-Kew Leong, Min-Yen Kan, Donghong Ji. Information Retrieval Technology/Third Asia Information Retrieval Symposium, AIRS 2006, Singapore, October 16-18, 2006. Proceedings, 2006, Springer
Levene, M. (2010). An Introduction to Search Engines and Web Navigation. Hoboken, N.J.: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=335281
Преподаватели
Архимандритов Игорь Борисович
Симарова Екатерина Николаевна
Course Syllabus
Abstract
Learning Objectives
Expected Learning Outcomes
Course Contents
Assessment Elements
Interim Assessment
Bibliography
Recommended Core Bibliography
Recommended Additional Bibliography
Authors