MS1PTS - Telecommunication Services Personalization

Course specification
Course title Telecommunication Services Personalization
Acronym MS1PTS
Study programme Electrical Engineering and Computing
Module Audio and Video Communications
Type of study master academic studies
Lecturer (for classes)
Lecturer/Associate (for practice)
Lecturer/Associate (for OTC)
    ESPB 6.0 Status elective
    Condition None
    The goal Introduce the students to the fundamentals and current state in personalized telecommunication services and applications. Introduce the students to the methods of content description, indexing and retrieval.
    The outcome The students will gain insight into design of the personalized services and applications. In particular, they will be able to formally describe the content features, quantify the mutual similarity of different items/services, and design personalized applications.
    Contents of lectures Meaning and importance of personalization. Applicable procedures. Ethical aspects and privacy protection. Modeling of users and contents. Information retrieval techniques. Vector space model. Machine learning techniques. Examples of personalized applications - web search, TV program guides, mobile applications.
    Contents of exercises Formal description of services and content. Information retrieval techniques and algorithms. Personalized applcation design.
    1. Christopher D. Manning, Prabhakar Raghavan, Hinrich Schütze: Introduction to Information Retrieval, Cambridge University Press, 2008 (Original title)
    2. Ricci, F.; Rokach, L.; Shapira, B.; Kantor, P.B. (Eds.): Recommender Systems Handbook, Springer, 2011 (Original title)
    3. Milan Bjelica, Ana Perić: “Adaptive Feedback Schemes for Personalized Content Retrieval”, IEEE Trans. Consumer Electron., 57 (3), August 2011, pp. 1251-1257. (Original title)
    Number of hours per week during the semester/trimester/year
    Lectures Exercises OTC Study and Research Other classes
    3 1
    Methods of teaching Lectures, practices, tests and study research work
    Knowledge score (maximum points 100)
    Pre obligations Points Final exam Points
    Activites during lectures 0 Test paper 20
    Practical lessons 10 Oral examination 20
    Projects 40
    Colloquia 10
    Seminars 0