Recommender Systems with Machine Learning - Generations of Recommender Systems

The video tutorial discusses the evolution of recommendation systems across three generations. The first generation includes knowledge-based, content-based, and collaborative filtering systems, along with hybrid systems. The second generation introduces matrix factorization, web usage mining, and personality-based approaches. The third generation focuses on advanced techniques like deep learning, product-based systems, and Boltzmann machines. The tutorial also highlights the significance of recommendation systems in artificial intelligence and explores how machine learning and deep learning enhance these systems.

Explain the role of deep learning in third generation recommended systems.

How can machine learning be utilized in recommended systems?