The engine code base is based on the DASE model. DASE is abbreviated as follows:
D: Data Source and Data Preparator
A: Algorithm
S: Serving
E: Evaluation Metric
The following table describes the function of each of the components and the associated Java classes for the Product Recommendation Engine:
| Component | Functionality | Java Class |
| Data Source and Data Preparator | The data Source reads data from the input source and transforms into the format used by the Preparator. The Data Preparator preprocesses the data and forwards it to the algorithm for model training. |
DataSource.java DataSourceParams.java Preparator.java |
| Algorithm | The Algorithm component includes the Machine Learning algorithm and the settings of its parameters, and determines how a predictive model is constructed. |
Algorithm.java AlgorithmParams.java |
| Serving | The Serving component takes prediction queries and returns prediction results. Serving can also support multiple algorithms by combining them. Business logic can be added in Serving to further customize the final returned results. |
Serving.java
|
| Evaluation Metric | An Evaluation Metric measures prediction accuracy by allocating a numerical score. It can be used to compare algorithms or algorithm parameter settings. |
EvaluationParameter.java EvaluationSpec.java PrecisionMetric.java |
The following diagram shows how the components documented in the table work together:
Let's dive deep into the code and check how one can customize the engine logic.
We have purposefully chosen both Java-based and Scala-based template engine code so that readers familiar with the simple object oriented language like Java or familiar with Scala can understand easily.