The recommendation engine has a lot of internal parameters for fine tuning the underlying algorithm. For example, when determining
the relevance of any given article, its popularity and freshness can alter its score (popular and recent articles score higher).
The weight of popularity and freshness when computing the article score can be controlled by setting the respective parameters
(beh.weight.popularity and beh.weight.freshness respectively) to better suit particular needs. There are many more parameters
to adjust, see the reference at the end of this document.
Parameters can be set per individual request or globally, for all requests
Both /recommend/contextual and /recommend/behavioural calls can be called with GET and POST HTTP methods. When POST-ing, some advanced parameters could be passed for fine tuning the results. Content-type for POST requests should be application/json and the request body should contain a simple JSON object with parameter name and value pairs. For example:
Getting and setting global parameter values goes through the /weight endpoint. GET returns a list of parameters and their
type, description and current value
PUT or POST set values. As in with parameters per recommendation call, the Content-type of the request should be
application/json and the request body should contain a simple object with name -> value pairs:
All recognized parameters, values below are the defaults: