Recommender Systems (H) COMPSCI4075

  • Academic Session: 2019-20
  • School: School of Computing Science
  • Credits: 10
  • Level: Level 4 (SCQF level 10)
  • Typically Offered: Semester 2
  • Available to Visiting Students: Yes
  • Available to Erasmus Students: Yes

Short Description

This course introduces the concepts, applications, algorithms, programming, and design of recommender systems -- software systems that recommend products or information, often based on extensive personalization. Recommender systems are widely used by many users on a day-to-day basis: while recommender systems have been pioneered by e-commerce sites such as Amazon and Netflix, they are widely used ranging in areas from marketing (e.g. personalized product recommendations with your supermarket loyalty card), education, social media, and financial services. This course discusses various techniques for making recommendations, including non-personalized, content-based, and collaborative recommendation techniques, and their evaluation.

Timetable

None

Requirements of Entry

Data Fundamentals (H) (or equivalent)

Text as Data - An Introduction to Document Analytics (H) (or equivalent)

Excluded Courses

Recommender Systems (M)

Co-requisites

None

Assessment

Examination 80%, coursework 20%.

Main Assessment In: April/May

Are reassessment opportunities available for all summative assessments? No

Reassessments are normally available for all courses, except those which contribute to the Honours classification. For non-Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below. 

 

Because feedback and solutions to the coursework will be provided, reassessment of the coursework is not possible.

Course Aims

Recommender systems are widely deployed stochastic retrieval systems - a search system that learns what to suggest to a user based upon no explicit user input (i.e. zero query), but make suggestions using what is known about the user or the demographics that the user belongs to. With the prevalence of recommender systems, from e-commerce sites to supermarket loyalty cards, it can be seen recommendation technologies have the potential to be more ubiquitous than search technologies. This course introduces the concepts, applications, algorithms, programming, and design of recommender systems -- software systems that recommend products or information, often based on extensive personalization. This course discusses various techniques for making recommendations that are widely used in industry, including non-personalized, content-based, and collaborative recommendation techniques, and their evaluation.

Intended Learning Outcomes of Course

By the end of this course students will be able to:

1. Describe the techniques in making automatic recommendations and personalising them for individual users.

2. Describe different recommendation system scenarios, and models suitable for deployment therein.

3. Discuss how a recommender system should be evaluated in terms of the system's performance and the user's satisfaction with the system.

4. Describe the practical engineering issues raised by the implementation of a recommender system in a real deployment.

5. Implement and evaluate a standard recommender system.

Minimum Requirement for Award of Credits

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.