This course provides a comprehensive introduction to Recommender Systems, focusing on how intelligent systems predict user preferences and suggest relevant items. Students learn the theoretical foundations, algorithms, and practical applications of recommendation techniques widely used in e-commerce, streaming platforms, social media, and online learning systems.
The course begins with an overview of recommendation problems, user–item interactions, and evaluation metrics. It then explores core approaches such as content-based filtering, collaborative filtering, and hybrid recommendation models. Learners gain insight into similarity measures, matrix factorization, and neighborhood-based methods, along with challenges like data sparsity, cold start, scalability, and privacy.
Practical components emphasize real-world implementation using datasets, basic machine learning models, and case studies from industry applications. Ethical considerations, bias, and fairness in recommendations are also discussed to promote responsible AI development.
By the end of the course, students will be able to design, implement, evaluate, and improve recommender systems, preparing them for roles in data science, machine learning, and intelligent system development.
- Teacher: KALA K U