Training Data Generation

Turn watch behavior into labels using explicit completion thresholds, then choose how to sample and weight the resulting examples. Keep those choices separate: the label defines the outcome, while the weight determines how strongly an example affects training.

As mentioned previously, you will build your model on implicit feedback from the user. You will look at how a user interacts with media recommendations to generate positive and negative training examples.

Generating training examples

One way of interpreting user actions as positive and negative training examples is based on the duration for which the user watched a particular show/movie. You take positive examples as ones where the user ended up watching most of a recommended movie/show, i.e., watched 80% or more. You take negative examples, again where we are confident that the user ignored a movie/show, i.e., watched 10% or less.