With exploration, Personalizer is able to continuously deliver good results, even as user behavior changes.

When Personalizer receives a Rank call, it returns a RewardActionID that either:

  • Uses known relevance to match the most probable user behavior based on the current machine learning model.
  • Uses exploration, which does not match the action that has the highest probability in the rank.

Personalizer currently uses an algorithm called epsilon greedy to explore.

Choosing an exploration setting

You configure the percentage of traffic to use for exploration in the Azure portal's Configuration page for Personalizer. This setting determines the percentage of Rank calls that perform exploration.

Personalizer determines whether to explore or use the model's most probable action on each rank call. This is different than the behavior in some A/B frameworks that lock a treatment on specific user IDs.

Best practices for choosing an exploration setting

Choosing an exploration setting is a business decision about the proportion of user interactions to explore with, in order to improve the model.

A setting of zero will negate many of the benefits of Personalizer. With this setting, Personalizer uses no user interactions to discover better user interactions. This leads to model stagnation, drift, and ultimately lower performance.

A setting that is too high will negate the benefits of learning from user behavior. Setting it to 100% implies a constant randomization, and any learned behavior from users would not influence the outcome.

It is important not to change the application behavior based on whether you see if Personalizer is exploring or using the learned best action. This would lead to learning biases that ultimately would decrease the potential performance.

Next steps

Reinforcement learning