COMP3607: RECOMMENDER SYSTEMS

• COMP2261 Artificial Intelligence AND COMP2271 Data Science

• 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.

• 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

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.

• 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.

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