Merge pull request #209437 from jeffmend/patch-6

Update concepts-exploration.md

Author: 19BMG00

articles/cognitive-services/personalizer/concepts-exploration.md

@@ -1,22 +1,22 @@
 ---
 title: Exploration - Personalizer
 titleSuffix: Azure Cognitive Services
-description: With exploration, Personalizer is able to continue delivering good results, even as user behavior changes. Choosing an exploration setting is a business decision about the proportion of user interactions to explore with, in order to improve the model.
+description: With exploration, Personalizer is able to continuously deliver good results, even as user behavior changes. Choosing an exploration setting is a business decision about the proportion of user interactions to explore with, in order to improve the model.
 author: jcodella
 ms.author: jacodel
 ms.manager: nitinme
 ms.service: cognitive-services
 ms.subservice: personalizer
 ms.topic: conceptual
-ms.date: 10/23/2019
+ms.date: 08/28/2022
 ---
 
-# Exploration and exploitation
+# Exploration and Known
 
-With exploration, Personalizer is able to continue delivering good results, even as user behavior changes.
+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 exploitation to match the most probable user behavior based on the current machine learning model.
+* 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. 
@@ -25,7 +25,7 @@ Personalizer currently uses an algorithm called *epsilon greedy* to explore.
 
 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 exploit with this probability on each rank call. This is different than the behavior in some A/B frameworks that lock a treatment on specific user IDs.
+Personalizer determines whether to explore or use the model's learned best action with this probability 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
 
@@ -35,8 +35,8 @@ A setting of zero will negate many of the benefits of Personalizer. With this se
 
 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 exploiting. This would lead to learning biases that ultimately would decrease the potential performance.
+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](concepts-reinforcement-learning.md) 
+[Reinforcement learning](concepts-reinforcement-learning.md)