Ad Prediction
Explore how to design robust ad prediction systems that adapt to changing data using online learning techniques. Understand model refreshing strategies, combine logistic regression with additive trees and neural networks to capture complex feature relationships, and learn to implement efficient training workflows for dynamic advertising environments.
We'll cover the following...
The ad prediction component has to make predictions for the final set of candidate selected ads. It needs to be robust and adaptive and should be able to learn from massive data volume.
Let’s go over the best setup and models for this problem.
Modeling approach
Ads are generally short-lived. So, our predictive model is going to be deployed in a dynamic environment where the ad set is continuously changing over time.
Given this change in an ad set, keeping the model up to date on the latest ads is important. In other words, model performance will degrade with each passing day if it isn’t refreshed frequently.
Online learning
If we have to plot the log loss of the model, it might look like the graph on the right. Here we are assuming that ...
