Recommendation Systems

Major: Systems and Methods of Decision Making
Code of subject: 7.124.01.O.008
Credits: 5.00
Department: Information Systems and Networks
Lecturer: Doctor of Sciences., Professor Lytvyn Vasyl? Volodymyrovych
Semester: 2 семестр
Mode of study: денна
Learning outcomes: As a result of studying the discipline, the student must be able to demonstrate the following learning outcomes: Specialized conceptual knowledge, which includes modern scientific achievements in the field of systems analysis and information technology and is the basis for original thinking and research. To develop intelligent systems in the conditions of poorly structured data of different nature. Know the basic models of recommendation systems and algorithms for their operation, classes of recommendation systems.
Required prior and related subjects: Technologies to support decision-making processes Distributed information systems
Summary of the subject: Study of the main classes of recommendation systems, recommendation models and algorithms, methods of evaluation and explanation of recommendations, mastering modern methods of design and development of specialized recommendation systems.
Assessment methods and criteria: • Current control (40%): written reports on laboratory work, oral examination; • Final control (60%). in written – 50%, verbally- 10%.
Recommended books: 1. C.C. Aggarwal: Recommender Systems: The Textbook – Springer, 2016. 2. D. Jannach, M. Zanker, A. Felfernig, G. Friedrich: Recommender Systems: An Introduction – Cambridge University Press, 2011. 3. F. Ricci, L. Rokach, B. Shapira (eds.): Recommender Systems Handbook, 2nd ed. – Springer, 2015. 4. K. Falk: Practical Recommender Systems – Manning Publications Co., 2019.

Recommendation Systems (курсова робота)

Major: Systems and Methods of Decision Making
Code of subject: 7.124.01.O.009
Credits: 2.00
Department: Information Systems and Networks
Lecturer: Doctor of Sciences., Professor Lytvyn Vasyl? Volodymyrovych
Semester: 2 семестр
Mode of study: денна
Learning outcomes: As a result of studying the discipline, the student must be able to demonstrate the following learning outcomes: Specialized conceptual knowledge, which includes modern scientific achievements in the field of systems analysis and information technology and is the basis for original thinking and research. To develop intelligent systems in the conditions of poorly structured data of different nature. Know the basic models of recommendation systems and algorithms for their operation, classes of recommendation systems.
Required prior and related subjects: Technologies to support decision-making processes Distributed information systems
Summary of the subject: Study of the main classes of recommendation systems, recommendation models and algorithms, methods of evaluation and explanation of recommendations, mastering modern methods of design and development of specialized recommendation systems.
Assessment methods and criteria: • Current control (40%): written reports on laboratory work, oral examination; • Final control (60%). in written – 50%, verbally- 10%.
Recommended books: 1. C.C. Aggarwal: Recommender Systems: The Textbook – Springer, 2016. 2. D. Jannach, M. Zanker, A. Felfernig, G. Friedrich: Recommender Systems: An Introduction – Cambridge University Press, 2011. 3. F. Ricci, L. Rokach, B. Shapira (eds.): Recommender Systems Handbook, 2nd ed. – Springer, 2015. 4. K. Falk: Practical Recommender Systems – Manning Publications Co., 2019.