SubjectsSubjects(version: 928)
Course, academic year 2022/2023
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Introduction to recommender systems and user preferences - NSWI166
Title: Úvod do doporučovacích systémů a uživatelských preferencí
Guaranteed by: Department of Software Engineering (32-KSI)
Faculty: Faculty of Mathematics and Physics
Actual: from 2022 to 2022
Semester: winter
E-Credits: 4
Hours per week, examination: winter s.:2/1, C+Ex [HT]
Capacity: unlimited
Min. number of students: unlimited
Virtual mobility / capacity: no
State of the course: taught
Language: Czech, English
Teaching methods: full-time
Additional information:
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
Incompatibility : NDBI037
Interchangeability : NDBI037
Is interchangeable with: NDBI037
Annotation -
Last update: RNDr. Filip Zavoral, Ph.D. (27.04.2021)
Due to the extreme information overload on the web, we need models that can process information in a personalized way. One class of such models are Recommender Systems (RS). The core of RS are machine learning algorithms focusing on user feedback. RS aim to predict users’ future preferences and provide them with surprising, yet relevant objects. This course covers common working principles of recommender systems, its learning methods, data types, requirements and evaluation as well as some aspects of the practical deployment.
Course completion requirements -
Last update: Mgr. Ladislav Peška, Ph.D. (24.09.2020)
  • oral exam (areas covered during lectures)
  • active participation on lectures and seminars
  • presentation of selected paper or individual project

  • participation on lectures/seminars may be substituted by more complex individual project
Literature -
Last update: Mgr. Ladislav Peška, Ph.D. (24.09.2020)
  • Ricci, F. et al (Eds): Recommender Systems Handbook, Springer, 2011
  • Jannach, D. et al (Eds): Recommender Systems: An Introduction, Cambridge University Press, 2011
  • Agarwal, D., & Chen, B. (2016). Statistical Methods for Recommender Systems. Cambridge University Press.
  • Proceedings of the Xth ACM Conference on Recommender Systems (2020 - 2007)

  • 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: Mgr. Ladislav Peška, Ph.D. (26.04.2021)
  • Introduction to Recommender Systems - mission, requirements, methods, data
  • User Feedback
  • Collaborative filtering, KNN, matrix factorization methods
  • Content-based filtering
  • representation of ordering, Fagin-Lotem-Naor (FLN) model
  • graphical form of FLN, Challenge-response framework
  • Hybrid and context-aware recommender systems
  • Evaluation of recommender systems, real-world applications

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