The video tutorial introduces the concept of autoencoders in recommender systems, focusing on how they learn non-linear representations of user-item matrices and reconstruct them using missing values. It explains the two main components: the encoder, which transforms user interactions into latent feature representations, and the decoder, which predicts user-item interaction probabilities. The course is designed to provide a foundational understanding of deep learning in recommender systems, with future videos covering strengths and limitations.

Explain how variational auto encoders are used in collaborative filtering.

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