Architectural Components
Explore the architectural components of recommendation systems by learning how candidate generation filters a large corpus efficiently and how ranking refines top recommendations for precision. Understand how training data is generated from user interactions to improve personalized movie suggestions.
We'll cover the following...
We'll cover the following...
It makes sense to consider generating the best recommendation from a large corpus of movies, as a multi-stage ranking problem. Let’s see why.
We have a huge number of movies to choose from. Also, we require complex models to make great, personalized recommendations. However, if we try to run a complex model on the whole corpus, it would be inefficient in terms of execution time and ...