Recommender Systems Complete Course Beginner to Advanced - Deep Learning Foundation for Recommender Systems: Embeddings

The video tutorial explains how deep learning recommendation models are built using techniques like factorization and embeddings. It describes embeddings as vectors representing entity features, ensuring similar entities have similar distances in vector space. An example is given with users rating movies, illustrating how features are created based on user-item interactions. The model learns user and item embeddings, determining distances in vector space. A function is developed to recommend items based on user context and embeddings. The tutorial concludes with a recap and hints at future topics.

Describe the significance of user context in recommendation systems.

What is the main goal of deep learning recommendation systems?

List some types of deep learning recommendation models.