Abstract: The present teaching relates to generating combination distributions for ads. A prediction model is obtained via machine learning with respect to a criterion. Training data are associated with multiple ads each having multiple attributes, and include combinations with recorded performance for each ad. Each combination has multiple assets representing respective attributes of an ad. Using the prediction model, performance of each combination of each ad can be predicted and used for generating combination distributions for the ads. Such generated combination distributions are then sent to an explore/exploit layer (EEL) at a frontend ad serving engine so that it can draw a combination associated with an auction winning ad for rendering on a webpage viewed by a user on a user device.