Recommender Systems

This lecture covers the evolution of recommender systems, including early recommendations, information retrieval, information filtering, and collaborative filtering. It discusses the impact of recommender systems on real-world applications, the principal stakeholders involved, and the various vocabulary terms and problem definitions related to recommendation systems. The lecture also delves into different types of recommendations, such as explicit and implicit feedback, star ratings, and preference release moments. It explores the challenges and frameworks of recommendation algorithms, including collaborative, content-based, and hybrid filtering. The lecture concludes with a discussion on the pros and cons of different recommendation paradigms and the importance of personalization and privacy in recommendation systems.