Advanced Recommender Systems

This course provides an introduction to advanced recommender system techniques used by digital platforms to personalise user experiences and improve decision-making. You will explore the algorithms and methodologies behind modern recommendation engines, from collaborative filtering and matrix factorisation to deep learning approaches. Through practical examples and real-world applications, you will gain an understanding of how recommender systems are designed, evaluated, and deployed across industries such as e-commerce, media, and online services. 

What you will learn: 

• Principles and architectures of modern recommender systems.  
• Collaborative filtering and matrix factorisation techniques.  
• Advanced recommendation methods using machine learning and deep learning. 
• Strategies for evaluating recommendation quality and performance.  
• Approaches to addressing challenges such as data sparsity, scalability, and cold-start problems.  
• Applications of recommender systems in real-world digital platforms.  

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