About the Learning API
Interact offers a learning module which uses a naive-bayesian algorithm to monitor visitor actions and propose optimal offers (in terms of acceptance). You can implement the same Java interface with your own algorithms using the Learning API to create your own learning module.
At the simplest level, the learning API provides methods to collect data from the runtime environment and to return an ordered list of recommended offers.
Learning API workflow
You can collect the following data from Interact
Campaign specific offer data
Configuration properties defined in the learning category for the design environment and the offerserving category for the runtime environment
You can use this data in your algorithms to create a list of proposed offers. You then return a list of recommended offers, in order of highest to lowest recommendation.
Although not shown in the diagram, you can also use the learning API to collect data for your learning implementation. You can keep this data in memory, or log it to a file or database for further analysis.
After creating your Java classes, you can convert them to jar files. Once you create your jar files, you must also configure the runtime environment to recognize your external learning module by editing configuration properties. You must copy your Java classes or jar files to every runtime server using your external learning module.
Besides the information in this guide, the JavaDoc for the learning optimizer API is available on any runtime server in the Interact/docs/learningOptimizerJavaDoc directory.
You must compile your implementation against interact_learning.jar located in the lib directory of your Interact runtime environment installation.
When writing your custom learning implementation, you should keep the following guidelines in mind.
To enable external learning
ILearning interface
IAudienceID interface
IInteractSessionData interface
IOfferCode interface
ITreatment interface
Learning API example