Recommender Systems

Personalized content recommendation is probably the most widely recognized and successful field of machine learning application in the real world. This course will discuss the concepts behind content recommender systems and how machine learning algorithms could help estimate and track user preference. Topics include a series recommender systems from classic, static, matrix factorization-based system to advanced, dynamic, deep learning-driven systems. Students will gain hands-on experience implementing large-scale recommender systems that meet the standards of real-world applications. They will also learn how to customize and optimize machine learning models for specific tasks by understanding practical constraints in real productions, such as efficiency, scalability requirements.