COMP54215: Recommender Systems

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• To introduce students to recommender systems techniques and approaches to evaluate them.
• To enable students to develop user profiles based on user demographics, behaviour, preferences, contextual information, etc.
• To train students to design and implement methods for predicting and recommending the most relevant and tailored content to an individual user.

• Levels of personalization; non-personalised recommenders.
• Content-based filtering.
• Collaborative filtering.
• Hybrid schemes.
• Context-aware recommenders.
• Knowledge-based and group recommenders.
• Conventional vs. state-of-the-art methods (e.g., deep learning in RS).
• Evaluation methods.
• Trends in RSs (e.g., cross-domain recommendations, fairness, popularity bias, etc.).
• Ethical issues in recommender systems.

Subject-specific Knowledge:

• By the end of this module, students should be able to demonstrate:
• a critical understanding of the different types of recommender systems, their purpose and domains of application.
• an understanding of recommender system users: interaction behaviour, demographics, preferences, contextual information.
• an in-depth knowledge of recommender system algorithms.
• a systematic understanding of recommender system evaluation methods.

Subject-specific Skills:

• By the end of this module, students should 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 conventional and state-of-the-art RS methods and techniques, but particularly content-based, collaborative, and hybrid recommender systems techniques.
• 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:

• By the end of this 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.
• Computer classes enable students to apply their learning to practical examples.
• Formative and summative assessments assess the knowledge in core recommender system concepts and application of the related methods and techniques.
• The assignment element of the coursework component consists of a coding exercise with accompanying report.
• The teacher-supervisor review element of the coursework component consists of student engagement in the course.

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