SubjectsSubjects(version: 837)
Course, academic year 2018/2019
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Advanced Methods for Recommender Systems - NSWI167
Title in English: Pokročilé metody doporučování
Guaranteed by: Department of Software Engineering (32-KSI)
Faculty: Faculty of Mathematics and Physics
Actual: from 2017
Semester: summer
E-Credits: 3
Hours per week, examination: summer s.:0/2 C [hours/week]
Capacity: unlimited
Min. number of students: unlimited
State of the course: taught
Language: Czech
Teaching methods: full-time
Guarantor: Mgr. Ladislav Peška, Ph.D.
Class: Informatika Bc.
Informatika Mgr. - volitelný
Classification: Informatics > Informatics, Software Applications, Computer Graphics and Geometry, Database Systems, Didactics of Informatics, Discrete Mathematics, External Subjects, General Subjects, Computer and Formal Linguistics, Optimalization, Programming, Software Engineering, Theoretical Computer Science
Annotation -
Last update: RNDr. Michal Kopecký, Ph.D. (12.05.2017)
This course extends knowledge from the „Introduction to Recommender Systems“ course by aiming on recent trends, challenges and approaches of this highly dynamic domain. The main part of the practical seminar will focus on developing solutions for real recommender systems challenges, e.g., recsyschallenge.com, www.clef-newsreel.org. Basic knowledge of preference learning and recommender systems method (see „Introduction to Recommender Systems“ course) is expected.
Course completion requirements -
Last update: Mgr. Ladislav Peška, Ph.D. (02.10.2017)
  • participation on a project
  • active participation on seminar
Literature -
Last update: RNDr. Michal Kopecký, Ph.D. (12.05.2017)
  • Ricci, F. et al (Eds): Recommender Systems Handbook, Springer, 2011
  • Jannach, D. et al (Eds): Recommender Systems: An Introduction, Cambridge University Press, 2011
  • Proceedings of the Recommender Systems Challenge, ACM, 2016, ISBN: 978-1-4503-4801-0
  • Proceedings of the 2015 International ACM Recommender Systems Challenge, ACM, 2015, ISBN: 978-1-4503-3665-9
  • Semantic Web Evaluation Challenge, Springer CCIS 445, 2014 DOI: 10.1007/978-3-319-12024-9

Syllabus -
Last update: RNDr. Michal Kopecký, Ph.D. (12.05.2017)
  • Fundamental methods in recommender systems - summary
  • New trends, challenges and approaches in recommender systems.
  • Specifics of preference learning application development
  • Introduction to recommending challenges - data, metrics, evaluation, goals
  • Teamwork on recommender systems development, individual consultations
  • Final evaluation, lessons learned, future challenges

 
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