NDAK22003U Web Recommender Systems (WRS)

Volume 2023/2024

The course objective is to offer an advanced introduction into Web Recommender Systems. The goal is to understand and model Web Information and to design and evaluate some of the major technologies operating in the area of Web Recommender Systems. Through applied projects, the course aims to stimulate and prepare students for their MSc thesis work.


Content in detail:

  • Basics of Recommender Systems (collaborative filtering and content based);
  • Evaluation of Recommender Systems;
  • Advanced Recommender Systems (knowledge-based, ensembled based, hybrid);
  • Exploiting additional sources of information for recommendation, e.g., context, location and time.


Learning Outcome


  • The basic models to develop a Web Recommender System
  • Techniques to exploy different sources of information in recommendation
  • Different criteria for the evaluation of Recommender Systems



Students should be able to transfer the above knowledge to real-world tasks by:

  • Designing appropriate strategies to develop Web Recommender Systems
  • Planning and carrying out appropriate evaluation
  • Diagnosing problems in standard Web Recommender Systems
  • Designing and calibrating solutions appropriate for expected usage loads



  • Explain basic principles and properties of Recommender Systems to both laymen and specialists
  • Use standard procedures and practices when designing or implementing Web Recommender Systems
  • Present evaluation analyses and results so that a technically qualified person can follow and obtain similar findings

The literature consists of seminal research and review articles from central journals and selected papers from peer-reviewed conferences, textbooks and research reports. This is supplemented with practical experience gained through lab sessions.

See Absalon for a list of literature.

It is expected that students know how to program and have a working knowledge of Machine Learning that can be obtained by any undergraduate course in Machine Learning. Examples of libraries that will be used at the labs: numpy, pandas, scikit-learn, matplotlib and other ML python libraries.

Academic qualifications equivalent to a BSc degree is recommended.
The course will use a combination of lectures (2 hours per week) and lab sessions (2 hours per week). Lectures and labs might include discussions, group activities, and student presentations. Where possible, relevant guest lecturers will be involved.

Students will carry-out a project which consists of both practical exercises (implementing state of the art solutions) and theoretical questions (to reflect on the course content in relation to the project). The project will cover the main topics presented during the lectures.
The course is identical to NDAK14004U Web Science (WS).
It is not allowed to pass both courses.
  • Category
  • Hours
  • Lectures
  • 32
  • Preparation
  • 80
  • Project work
  • 71
  • Exam Preparation
  • 22
  • Exam
  • 1
  • Total
  • 206
Feedback by final exam (In addition to the grade)
7,5 ECTS
Type of assessment
Written assignment
Oral examination, 20 min.
Type of assessment details
Specifically, the exam consists of two parts:

1. An individual report based on the project (written assignment).
2. An individual oral examination (without preparation) based on the report and project

The written and oral examination are not weighted, why only one overall assessment is provided for the two parts of the exam.
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners.

Same as ordinary exam.

For the re-exam the student must complete a new project and submit a new report. The deadline for submitting the new report will be published in Absalon.

Additionally the 20-minutes oral examination without preparation will be administered covering the full course syllabus.

The written and oral examination are not weighted, why only one overall assessment is provided for the two parts of the exam.

It is not possible to reuse parts of the exam at a later exam.


Criteria for exam assesment

See Learning Outcome.