COMP47580 Recommender Systems & Collective Intelligence

Academic Year 2022/2023

Recommendation technologies have become an important part of our online experiences, helping us to discover books, movies, and music that are relevant to our likes and preferences. So much so, in fact, that recommender systems are now a fundamental component of most e-commerce platforms, streaming services, and other content sites. At their core recommender systems operate by learning about the likes and dislikes of individuals and groups of users so that they may proactively tailor content for these users.

In this course we will cover the fundamentals of recommender systems technologies including the main approaches to building and evaluating recommender systems (content-based vs collaborative filtering vs hybrid approaches) as well as a variety of more advanced topics.

This module will be assessed by continuous assessment only which will take the form of in-class tests and practical projects and reports related to the development of recommender systems technologies.

Please note that proficiency in the Java Programming Language is required.

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Curricular information is subject to change

Learning Outcomes:

On successful completion of this module the learner will be able to:

- Understand the typical recommender system architecture and recommendation tasks.
- Understand core algorithms driving common recommender systems including the pros and cons of each.
- Learn about different approaches to evaluating recommender systems, using a variety of metrics and methodologies.
- Learn about more contemporary recommender systems research covering a variety of more advanced topics.

Student Effort Hours: 
Student Effort Type Hours




Autonomous Student Learning




Approaches to Teaching and Learning:
This module will involve a combination of lectures and active/task-based learning.
Requirements, Exclusions and Recommendations
Learning Requirements:

Proficiency in the Java Programming Language is required. There is a significant software engineering effort required and so students must be comfortable and proficient in developing complex programs using advanced tools and techniques.

Module Requisites and Incompatibles
COMP30490 - Collective Intelligence, COMP40320 - Recommender Systems, COMP41440 - Collective Intelligence

Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Continuous Assessment: In-class test Throughout the Trimester n/a Alternative linear conversion grade scale 40% No


Continuous Assessment: Practical projects Throughout the Trimester n/a Alternative linear conversion grade scale 40% No


Continuous Assessment: Practical report Throughout the Trimester n/a Graded No


Carry forward of passed components
Resit In Terminal Exam
Summer No
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Post-assessment, feedback will be provided to students in class. Individual feedback is also available to students. During practical sessions, a teaching assistant and demonstrators will be available to provide assistance and feedback to students on their work.

Name Role
Aayush Roy Tutor
Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Practical Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Thurs 10:00 - 11:50
Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Wed 14:00 - 15:50