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Course, academic year 2018/2019
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Introduction to Recommender Systems - NSWI166
Title in English: Úvod do doporučovacích systémů
Guaranteed by: Department of Software Engineering (32-KSI)
Faculty: Faculty of Mathematics and Physics
Actual: from 2017
Semester: winter
E-Credits: 4
Hours per week, examination: winter s.:2/1 C+Ex [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. (11.05.2017)
Recommender systems are both an interesting research topic as well as an important commercial application. The main task of recommender systems is to provide user with surprising, hard to find yet relevant objects. Recommender systems are employed alongside with common search engines on numerous enterprises on the web and beyond. This course covers common working principles of recommender systems, its learning methods, data types, requirements and evaluation.
Course completion requirements -
Last update: Mgr. Ladislav Peška, Ph.D. (02.10.2017)
  • oral exam (areas covered during lectures)
  • active participation on seminars
  • presentation of selected paper or individual project

  • participation on seminars may be substituted by more complex individual project
Literature -
Last update: RNDr. Michal Kopecký, Ph.D. (11.05.2017)
  • Ricci, F. et al (Eds): Recommender Systems Handbook, Springer, 2011
  • Jannach, D. et al (Eds): Recommender Systems: An Introduction, Cambridge University Press, 2011
  • Marius Kaminskas, Derek Bridge, Franclin Foping and Donogh Roche: Product-Seeded and Basket-Seeded Recommendations for Small-Scale Retailers, Journal on Data Semantics, pp.1-12, 2016.
  • Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. BPR: Bayesian personalized ranking from implicit feedback. In UAI '09. AUAI Press, 2009, 452-461.
  • Nguyen, J. & Zhu, M. Content-boosted matrix factorization techniques for recommender systems. Statistical Analysis and Data Mining, Wiley Subscription Services, Inc., A Wiley Company, 2013, 6, 286-301
  • Gorgoglione, M.; Panniello, U. & Tuzhilin, A.: The effect of context-aware recommendations on customer purchasing behavior and trust. Proceedings of the fifth ACM conference on Recommender systems, ACM, 2011, 85-92

Syllabus -
Last update: RNDr. Michal Kopecký, Ph.D. (11.05.2017)
  • Introduction to Recommender Systems - mission, requirements, methods, data
  • Collaborative filtering and matrix factorization methods
  • Content-based filtering
  • Linked Open Data in recommender systems
  • User Feedback
  • Hybrid and context-aware recommender systems, handheld devices
  • Explanations, trust, social networks
  • Evaluation of recommender systems, real-world applications

 
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