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Archived Curricula Guide 2017–2019
Curricula Guide is archieved. Please refer to current Curricula Guides
TIETS43 Recommender Systems 5 ECTS
Organised by
Degree Programme in Computer Sciences
Preceding studies
This course is self-contained. Good understanding on topics related to databases, information retrieval and big data will be useful.

General description

Recommender systems have become indispensable for several Web sites, such as Amazon, Netflix and Google News, helping users navigate through the abundance of items. In general, recommender systems facilitate the selection of data items by users, by issuing recommendations for items they might like. Nowadays, there are numerous recommendation approaches, like neighborhood-based approaches and model-based ones, and a lot of work on specific aspects of recommendations, like the cold start problem, and the evaluation of the recommended items in terms of a variety of parameters, like relevance, surprise and diversity. Also, more recently, recommendations have more broad applications, beyond products, like news recommendations, friends recommendations, medicine recommendations, query recommendations, and others.

In this course, we will focus on algorithmic approaches for producing recommendations, such as collaborative and content-based filtering. We will also discuss how to measure the effectiveness of recommender systems. Finally, we will cover emerging topics, such as contextual recommendations, recommendations for groups, packages recommendations, and how we can achieve diversity in recommender systems.

Learning outcomes

After completing the course, the student is expected to:
- know the basic concepts and techniques of recommender systems, including collaborative and content-based filtering techniques, and techniques for computing contextual recommendations, recommendations for groups, packages recommendations and diverse recommendations,
- be able to handle contemporary research issues and problems on recommender systems, and
- solve real-world problems.


Collaborative Filtering, Content-based Filtering, Knowledge-based Recommendations, Contextual Recommendations, Recommendations for Groups, Packages Recommendations, Explanations in Recommender Systems, Diversity in Recommender Systems.

Teaching methods

Teaching method Contact Online
Independent work

Lectures, exercises, student presentations in class, programming project.

Teaching language


Modes of study

Option 1
Available for:
  • Degree Programme Students
  • Other Students
  • Open University Students
  • Doctoral Students
  • Exchange Students
Participation in course work 
In English


Numeric 1-5.

Belongs to following study modules

Faculty of Natural Sciences
Faculty of Natural Sciences
Faculty of Natural Sciences
Faculty of Natural Sciences
Archived Teaching Schedule. Please refer to current Teaching Shedule.
Faculty of Natural Sciences