15. The apparatus method of claim 14 , wherein the application is configured to train a model by: representing the sample user behavior as an input node having an input weight; representing the sample user attribute as an output node having an output weight; and determining one or more functions based using one or more processing nodes, wherein the one or more functions are configured to produce the output weight based on the input weight.
15. The apparatus method of claim 14 , wherein the application is configured to train a model by: representing the sample user behavior as an input node having an input weight; representing the sample user attribute as an output node having an output weight; and determining one or more functions based using one or more processing nodes, wherein the one or more functions are configured to produce the output weight based on the input weight. 16. The apparatus of claim 15 , wherein the application is further configured to train by combining additional sample user behaviors and additional sample user attributes from multiple users with the sample user behavior and sample user attribute.