PV254 Recommender Systems

Faculty of Informatics
Autumn 2017
Extent and Intensity
1/1/0. 2 credit(s) (plus extra credits for completion). Type of Completion: k (colloquium).
Teacher(s)
doc. Mgr. Radek Pelánek, Ph.D. (lecturer)
Guaranteed by
doc. RNDr. Aleš Horák, Ph.D.
Department of Machine Learning and Data Processing – Faculty of Informatics
Supplier department: Department of Machine Learning and Data Processing – Faculty of Informatics
Timetable
Tue 8:00–9:50 A318
Prerequisites
Programming skills, mathematics at the level of MB101-MB104 courses.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 30 student(s).
Current registration and enrolment status: enrolled: 0/30, only registered: 0/30, only registered with preference (fields directly associated with the programme): 0/30
fields of study / plans the course is directly associated with
there are 16 fields of study the course is directly associated with, display
Course objectives
The goal of the course is to familiarize students with basic techniques and problems in the field of recommender systems. The course is project based - students have practical experience with development of a simple recommender system or with a partial evaluation of a realistic recommender system.
Learning outcomes
At the end of the course students will understand the main types of recommender systems and their application domains; be able to apply the basic recommender techniques; be able to implement basic versions of recommender techniques; understand main aspects of evaluation of recommender systems and be able to analyze such evaluations.
Syllabus
  • Recommender systems, motivation, applications in different domains.
  • Types of recommender systems: non-personalized, content based, collaborative filtering.
  • Techniques and algorithms for recommender systems, particularly with focus on collaborative filtering (user-user, item-item, SVD).
  • Evaluation: methodology, types of experiments, evaluation metrics, examples.
  • Other aspects of recommender systems (e.g., explanations of recommendations, trust, attacts).
  • Case studies (e.g., Amazon, Netflix, Google News, YouTube).
  • Educational recommender systems, current research at Faculty of informatics.
Literature
    recommended literature
  • JANNACH, Dietmar. Recommender systems : an introduction. 1. pub. New York: Cambridge University Press, 2011, xv, 335. ISBN 9780521493369. info
  • Recommender systems handbook. Edited by Francesco Ricci. New York: Springer, 2011, xxix, 842. ISBN 9780387858203. info
Teaching methods
The course consist of lectures and a project. The project can be either an implementation of a simple recommender system or an evaluation of one of the described techniques on data from real recommender systems (e.g., Netflix data, faculty projects).
Assessment methods
The main part of the evaluation is a project. The project is typically in groups (2-4 students), but individual projects are also possible.
Language of instruction
English
Further Comments
Study Materials
The course is taught annually.
Teacher's information
http://www.fi.muni.cz/~xpelanek/PV254/
The course is also listed under the following terms Autumn 2014, Autumn 2015, Autumn 2016, Autumn 2018, Autumn 2019, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.
  • Enrolment Statistics (Autumn 2017, recent)
  • Permalink: https://is.muni.cz/course/fi/autumn2017/PV254