Huge number of Impression in digital adaptive model degrading the propensity and model performance
I am looking for some guidance on the best practice / way to define impressions as a outcome for adaptive in digital channel. In our application we have configured impressions as negative and click as positive, the problem we are facing is that the ratio of impression to click (negative to positive) is huge because as per our business rules we show proposition X number of times before supressing it.
So the volume of impression can go X times the volume of click. Due to this the ratio between negative to positive is huge and the propensity is very low and model performance is also not good.
I want to know how can we balance the ratio between positive and negative outcome? Do we really need to try and balance the ratio or Pega takes care of it internally? If so how can we overcome the problem of low propensity?
Also, we tried another approach to reduce the negative to positive response ratio by grouping multiple impressions before feeding it to adaptive model, here we are grouping X impression and writing it to Adaptive model once. These approach is taken to bring down the count of negative responses and have only one negative response for X impressions. I want to understand if this approach is going to be fine or will result in some problem when we upgrade our system to 8.5 from current 7.3 version.