In today’s digital economy, AI-driven recommendation systems (RecSys) are at the heart of successful businesses, powering personalization in e-commerce, media, fintech, and beyond. From boosting customer engagement to increasing revenue through targeted recommendations, organizations rely on machine learning-powered RecSys to drive business outcomes. This course provides a practical, industry-focused approach to designing and deploying next-generation recommender systems. Participants will explore the evolution from traditional (discriminative) to cutting-edge (generative) AI approaches, gaining hands-on experience with collaborative filtering, deep learning-based recommendations, and generative AI for hyper-personalization.

Through real-world case studies in e-commerce, streaming services, and healthcare, attendees will learn how to:

• Build scalable and business-driven recommender systems
• Optimize personalization strategies to increase engagement and revenue
• Address bias, fairness, and ethical concerns in AI-powered personalization
• Leverage Generative AI for dynamic content and user experience enhancement

Introduction to Personalization and Recommender Systems

• Collaborative Filtering,
• Content-based Filtering,
• Hybrid

Computational methods for designing Recommender Systems (Discriminative modelling)

• Learning representations from data
• Unimodal and Multimodal representation learning
• Transfer Learning
• Neural Topic Modelling
• Matrix Factorization and Singular Value Decomposition (SVD)

Introduction to Generative AI for Recommender Systems:

• Autoencoders
• Variational Autoencoders (VAEs),
• Generative Adversarial Networks( GANs)

Introduction to Reinforcement Learning (RL) for Recommender Systems

• Contextual Bandits for Personalized Recommendations
• Multi-Armed Bandit Problem and Real-Time Recommendations

Human-Centered Recommendation Systems

• Why Human-centered RecSys
• Human-centred RecSys design pipeline
• Multi-stakeholder awareness in Recommender systems
• Context-aware recommender systems

Evaluating Recommender systems

• Evaluation Metrics
• Offline Evaluation
• Conducting a User study
• Online Evaluation

• Develop foundational knowledge of Recommender Systems (RecSys), including both discriminative and generative approaches.
• Understand a wide variety of RecSys algorithms and their application across diverse domains, including healthcare and visual arts.
• Design and evaluate human-centred RecSys, incorporating principles of user experience, ethical AI, and interdisciplinary considerations.
• Apply practical skills to design, implement, and evaluate RecSys using real-world datasets, with a focus on personalization and innovative solutions.
• Analyze case studies to understand the role of RecSys in addressing challenges and propose solutions

An intensive week with theory in the mornings and hands-on in the afternoons. This balanced approach helps participants understand both the technical details and business impact, giving them practical  and useful insights to apply AI-powered personalization in their careers or workplaces.