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
Homework
Project
The final assessment for the course is conducted in the format of presenting the final project. Each student (or team) presents the developed search system or its key components, demonstrates the obtained results (quality metrics, model behavior in the contest, comparison with baselines), and answers questions from the instructor and/or the committee.
The project presentation includes a brief structured presentation of the problem statement, the data used, the selected models and architectures, the experiments, the analysis of metrics and limitations of the solution, as well as a discussion of possible improvements and further development of the system.
Interim Assessment
2026/2027 2nd module
Итоговая оценка за курс формируется на основе накопленной балльной системы и затем переводится в 10‑балльную шкалу НИУ ВШЭ.
Общая сумма баллов по курсу:
Окурс = Одз + Опроект
Одз — суммарная оценка за четыре домашних задания (максимум 50 баллов).
Опроект — оценка за финальный проект (максимум 50 баллов).
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
Course Syllabus
Abstract
Learning Objectives
Expected Learning Outcomes
Course Contents
Assessment Elements
Interim Assessment
Bibliography
Recommended Core Bibliography
Recommended Additional Bibliography
Authors