Candidate Generation and Ranking Model

3. Multi-stage models

There are two stages, candidate generation, and ranking. The reason for two stages is to make the system scale.

It’s a common pattern that you will see in many ML systems.

We will explore the two stages in the section below.

• The candidate model will find the relevant videos based on user watch history and the type of videos the user has watched.
• The ranking model will optimize for the view likelihood, i.e., videos that have high a watch possibility should be ranked high. It’s a natural fit for the logistic regression algorithm.

Candidate generation model

Feature engineering

• Each user has a list of video watches (videos, minutes_watched).

Training data

• For generating training data, we can make a user-video watch space. We can start by selecting a period of data like last month, last 6 months, etc. This should find a balance between training time

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