The video tutorial introduces the concept of autoencoders in recommender systems, focusing on how they learn non-linear representations of user-item matrices. It explains the structure of autoencoders, consisting of encoder and decoder networks, and how these networks transform user interactions into latent feature representations. The tutorial also covers variational autoencoders for collaborative filtering and concludes with an overview of the course and upcoming topics.

Explain how variational auto encoders are used in collaborative filtering.

What are the strengths and limitations of deep learning recommender systems?