194.035 Recommender Systems
This course is in all assigned curricula part of the STEOP.
This course is in at least 1 assigned curriculum part of the STEOP.

2021S, VU, 2.0h, 3.0EC


  • Semester hours: 2.0
  • Credits: 3.0
  • Type: VU Lecture and Exercise
  • Format: Distance Learning

Learning outcomes

After successful completion of the course, students are able to:

(1) comprehend the basic concepts of recommender systems,

(2) distinguish the differences among various recommendation methods,

(3) “recommend” appropriate recommendation techniques and evaluation strategies when faced with a task,

(4) implement basic recommendations methods and evaluate them over real datasets and tasks.

Subject of course

  • Introduction
  • Collaborative Filtering (CF)
  • Model-based CF -- Matrix Factorization
  • Content-based Recommenders
  • Evaluation Methods
  • Sequence-aware Recommenders
  • Special Topics (e.g., Fairness, Group Recommenders, Special Domains - Tourism, Music, News)

Teaching methods

The programming assignments are to be done in Python using Jupyter Notebooks. A short introduction to using Jupyter Notebooks will be given. In class, there will be a discussion of the assignments, solving any problems encountered, and at then end the solutions will be overviewed.

The project is to be done in any programming language and environment. There will be dedicated lectures for the project. Also, students will be motivated (by bonus points) to actively help each other.

Mode of examination


Additional information

This course is an overview of the general research area of Recommender Systems. The goal of these systems is to address the information overload problem (multitude of choices) people face in everyday life. Examples include selecting news articles to read, a movie to watch, a travel destination, friends to connect with, a restaurant to dine, buying a product.

The course will introduce the basic concepts, that is, users, items, preferences, explicit/implicit feedback, and proceed to explain important tasks, such as modeling a user’s preferences and an item’s attractiveness, collecting feedback from users, predicting the degree of interest of a user for an item, evaluating effectiveness. For these tasks the course will overview the most important approaches taken, and discuss the state-of-the-art. Towards the end of the course, certain advanced specialized topics, recently being investigated by the research community, will be discussed.

The students will be asked to implement simple approaches using real-life datasets, and work on a real-case task (such as an ACM RecSys Challenge).



Course dates

Tue10:00 - 12:0002.03.2021 - 29.06.2021 Zoom Meeting (LIVE)Lecture (online)
Recommender Systems - Single appointments
Tue02.03.202110:00 - 12:00 Zoom MeetingLecture (online)
Tue09.03.202110:00 - 12:00 Zoom MeetingLecture (online)
Tue16.03.202110:00 - 12:00 Zoom MeetingLecture (online)
Tue23.03.202110:00 - 12:00 Zoom MeetingLecture (online)
Tue13.04.202110:00 - 12:00 Zoom MeetingLecture (online)
Tue20.04.202110:00 - 12:00 Zoom MeetingLecture (online)
Tue27.04.202110:00 - 12:00 Zoom MeetingLecture (online)
Tue04.05.202110:00 - 12:00 Zoom MeetingLecture (online)
Tue11.05.202110:00 - 12:00 Zoom MeetingLecture (online)
Tue18.05.202110:00 - 12:00 Zoom MeetingLecture (online)
Tue01.06.202110:00 - 12:00 Zoom MeetingLecture (online)
Tue08.06.202110:00 - 12:00 Zoom MeetingLecture (online)
Tue15.06.202110:00 - 12:00 Zoom MeetingLecture (online)
Tue22.06.202110:00 - 12:00 Zoom MeetingLecture (online)
Tue29.06.202110:00 - 12:00 Zoom MeetingLecture (online)

Examination modalities

The course involves programming assingments and a project, all in groups of 3-5 students, and a final written exam.

- 4 Programming Assignments in Python using Jupyter Notebooks, where you fill in the missing code. (20% of total grade)

- 1 Project in any programming language. (30% of total grade)

- The written online exam is open book with open questions and some T/F statements. (50% of total grade)

Course registration

Begin End Deregistration end
11.02.2021 09:00 15.03.2021 23:59 28.03.2021 23:59


Study CodeSemesterPrecon.Info
066 645 Data Science
066 926 Business Informatics


No lecture notes are available.

Previous knowledge

  • basic knowledge of Linear Algebra, Calculus and Statistics
  • background in Machine Learning, Information Retrieval, E-Commerce is welcome but not required
  • all necessary concepts are introduced in course
  • content on slides alone suffice
  • programming skills required