Web Recommender Systems (WRS)

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:

  • 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.

 

Education

MSc Programme in Computer Science

Learning outcome

Knowledge

  • 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

 

Skills

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

 

Competences

  • 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 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.

The course is identical to NDAK14004U Web Science (WS).
It is not allowed to pass both courses.

Oral
Feedback by final exam (In addition to the grade)
ECTS
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.
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
  • 32
  • Preparation
  • 80
  • Project work
  • 71
  • Exam Preparation
  • 22
  • Exam
  • 1
  • English
  • 206

Kursusinformation

Language
English
Course number
NDAK22003U
ECTS
7,5 ECTS
Programme level
Full Degree Master
Duration

1 block

Placement
Block 3
Schedulegroup
B2
Capacity
No limit
The number of seats may be reduced in the late registration period
Studyboard
Study Board of Mathematics and Computer Science
Contracting department
  • Department of Computer Science
Contracting faculty
  • Faculty of Science
Course Coordinator
  • Maria Maistro   (2-7a7a4d71763b78823b7178)
Saved on the 28-02-2022

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