Architectural Components
Explore the architectural components of recommendation systems, focusing on a two-stage process: candidate generation for high recall and ranking for high precision. Understand how to efficiently handle large data corpora and personalize recommendations using user interactions and engagement data.
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 computing resources usage.
Therefore, we split the recommendation task into ...