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Durham University

Faculty Handbook 2022-2023

Module Description

Please ensure you check the module availability box for each module outline, as not all modules will run each academic year.

Department: Computer Science

COMP3607: RECOMMENDER SYSTEMS

Type Open Level 3 Credits 10 Availability Available in 2022/23 Module Cap None. Location Durham

Prerequisites

  • (COMP2261 Artificial Intelligence AND COMP2271 Data Science) OR COMP2231 Software Methodologies

Corequisites

Excluded Combination of Modules

Aims

  • Have you ever wondered how Netflix, YouTube, Amazon or Spotify make suggestions for which content next to view?
  • In this module, we will look at the inner workings of recommender systems;
  • explore developing user profiles based on demographics, preferences, context, etc.;
  • and put to practice approaches to predict the "best" content to recommend to an individual user.

Content

  • Non-personalised recommenders
  • Content-based filtering
  • Collaborative filtering
  • Context-aware recommenders
  • Other RS types, e.g.: hybrid and group
  • Evaluation methods
  • Ethical issues in recommender systems

Learning Outcomes

Subject-specific Knowledge:
  • On completion of the module, students will be able to demonstrate:
  • an understanding of the different types of recommender systems, their purpose and domains of application
  • an understanding of recommender system users: usage behaviour, demographics, preferences, contextual information
  • an in-depth knowledge of recommender system algorithms, specifically non-personalised, content-based and collaborative filtering, hybrid techniques and context-aware recommenders
  • an understanding of recommender system evaluation methods.
Subject-specific Skills:
  • On completion of the module, students will be able to demonstrate:
  • an ability to undertake self-study and independent research in recommender system concepts, state-of-the-art techniques, and exploration of potential for further developments
  • an ability to apply methods and techniques from non-personalised, content-based, collaborative, hybrid and context-aware recommender systems
  • an ability to implement a recommender system for a specific domain
  • an ability to evaluate the performance of different recommender systems, including any ethical issues they might cause.
Key Skills:
  • On completion of the module, students will be able to demonstrate:
  • an ability to critically analyse and evaluate current practices and recent advances in Computer Science and IT
  • an ability to identify the applicability of Computer Science methods to resolve challenges or achieve goals in a specific domain
  • an ability to practically implement Computer Science techniques/methods
  • an ability to work in teams and perform peer-review.

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

  • Lectures enable the students to learn new material relevant to recommender system concepts, methods, advances and their applications in different domains.
  • Formative and summative assignments assess the knowledge in core recommender system concepts and application of the related methods and techniques.

Teaching Methods and Learning Hours

Activity Number Frequency Duration Total/Hours
lectures 20 2 per week 1 hour 20
preparation and reading 80
total 100

Summative Assessment

Component: Coursework Component Weighting: 100%
Element Length / duration Element Weighting Resit Opportunity
Summative Assignment 100% No

Formative Assessment:

Example formative assignments are given during the course. Additional revision lectures may be arranged in the 3rd term.


Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University



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