Web Science (WS)

Course content

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
Education

MSc Programme in Computer Science

Learning outcome

Knowledge

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

 

Skills

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

 

Competences

  • 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 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 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.

Written
Individual
Continuous feedback during the course of the semester
Peer feedback (Students give each other feedback)
ECTS
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.
Aid
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners.
Criteria for exam assessment

See Learning Outcome.

Single subject courses (day)

  • Category
  • Hours
  • Lectures
  • 28
  • Preparation
  • 15
  • Theory exercises
  • 56
  • Practical exercises
  • 56
  • Project work
  • 50
  • Exam
  • 1
  • English
  • 206