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Curricular information is subject to change
Learning outcomes:
- Students will understand the typical recommender system architecture and recommendation tasks.
- Students will understand core algorithms driving common recommender systems including the pros and cons of each.
- Students will learn about different approaches to evaluating recommender systems, using a variety of metrics and methodologies.
- Students will also learn about more contemporary recommender systems research covering a variety of more advanced topics including opinion mining, explanation, alternative ranking strategies, robustness etc.
- Students will build and evaluate their own recommender systems during the course of the module.
Student Effort Type | Hours |
---|---|
Lectures | 24 |
Practical | 22 |
Autonomous Student Learning | 150 |
Total | 196 |
Proficiency in the Java Programming Language is required.
Description | % of Final Grade | Timing |
---|---|---|
Continuous Assessment: Practical coding project and report (foundational and advanced topics) | 50 |
Throughout the Trimester |
Continuous Assessment: Paper (foundational and advanced topics) | 50 |
Varies over the Trimester |
Compensation
This module is not passable by compensation
Resit Opportunities
In-semester assessment
Remediation
If you fail this module you may resit or retake the next time the module is offered
Name | Role |
---|---|
Professor Barry Smyth | Lecturer / Co-Lecturer |
Mrs Nina Hagemann | Tutor |