NDAK14004U Web Science (WS)

Volume 2021/2022

MSc Programme in Computer Science


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:

  • The World Wide Web and its challenges
  • Collective intelligence and crowdsourcing
  • Recommender systems
  • Data analytics for Recommender Systems
  • Advanced topics in Recommender Systems
Learning Outcome


  • The basic models and techniques of mining information on the Web
  • Different criteria for analytics applications



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

  • Designing appropriate strategies for crawling, mining and analysing Web information
  • Planning and carrying out appropriate evaluations
  • Diagnosing problems in standard Web Recommender Systems
  • Designing and calibrating solutions appropriate for expected usage loads



  • Explain basic Web principles and properties 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.

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.
  • Category
  • Hours
  • Lectures
  • 28
  • Preparation
  • 15
  • Theory exercises
  • 56
  • Practical exercises
  • 56
  • Project work
  • 50
  • Exam
  • 1
  • Total
  • 206
Continuous feedback during the course of the semester
Peer feedback (Students give each other feedback)
7,5 ECTS
Type of assessment
Written assignment
Oral examination, 20 min.
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 submit a new report no later than three weeks before the re-exam

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.