This section is devoted to users that want to create their own models and adjust their parameters and functions. There are two key elements in our models:
Target represents what you query with and
output_type is what you want returned by your query.
Other elements that can be set to optimize the model are
Features define which attributes of your object the model will use, i.e.
model_type (not to be confused with the
model_namethat is used in the Out of the Box models discussed in the Simple ML section) defines the mathematical model used to optimise your ML model’s performance, depending on your data.
Target is the element you want to give your model while
output_type is the element you want to get back from your model.
Example: When you build a Product Recommendation for Users, you give the recommender a
user and get back a
product. In this case the
user is set as the
product is set as the
Features can be any combination of
Model_type is the mathematical model the prediction is based on.
Recommendation models by nature are dependant upon the transaction history you have of your customers’ purchases or behavior. You can combine the
output_type to build any type of recommender.
We provide four recommendation models: Collaborative Filtering, Ranking, Content and Popularity. You can set these as the
model_type to create a variety of recommenders.
simple_product_recommender can also be implemented setting
In this demo, the
target is a
user and the
output_type is a
product (or movies in this case), the
ranking_recommenderand there are no
There are two types of Similarity models; those that compare content of
features and those that are based on the transaction history.
You can create a Content Similarity model based on the
features of the User or Product Building Blocks. Pick an
output_type and at least one
description, and set the
When you query a model, you will always get the
output_type back, i.e., Product or User. But, you can also query the actual
features of those Blocks themselves by providing those instead of an object id.
If you build a User Similarity model based on
tags, you can query any set of
tags to get
users with similar
This model uses similarity in the same manner as the Collaborative Filtering model to return similar modules when the
target module and the
output_type module are different. In this case, provide a
output_typejust like in the Recommendation Engines section, but set the
Get similar products based on their names by setting