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: Execution of the course work makes it possible to achieve the normative content of training of higher education, formulated in the program learning outcome, namely: • Know and be able to apply in practice the methods of systems analysis, methods of mathematical and information modeling to build and study models of objects and processes of informatization. • Be able to develop expert and recommendation 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 During the course work, students must learn to work independently with the literature and with modern tools for designing, creating and applying recommendation systems.
Required prior and related subjects: Technologies to support decision-making processes Distributed information systems
Summary of the subject: Theme of work Recommended system of music preferences of the user Recommended system of literary preferences of the user Recommended system for determining user preferences for watching feature films Recommended system of sports preferences of the user Recommended system for determining user preferences for viewing news Recommended system for determining user preferences for visiting sites Recommended system for determining the list of sporting goods for purchase by the user of the web store Recommended system for determining the list of books for purchase by the user of the web store Recommended system for determining the list of CDs for purchase by the user of the web store Recommended system for determining the list of seedlings for purchase by the user of the web store
Assessment methods and criteria: • Final control (100%). work protection
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.