Multi-source data and explainable machine learning systems and methods

A non-transitory computer-readable storage medium for tangibly storing computer program instructions capable of being executed by a computer processor, the computer program instructions defining steps of:

• receiving data from a plurality of heterogeneous data sources;
• processing each type of received data through a corresponding type-specific processing pipeline by performing temporal pattern extraction specific to each data type;
• generating cross-source feature vectors by performing temporal correlation analysis across the processed data from different data sources;
• training an ensemble machine learning model using the cross-source feature vectors;
• generating engagement predictions and feature contribution values that quantify an impact of each feature on each prediction;
• presenting the engagement predictions with natural language explanations based on the feature contribution values;
• and
• updating weights of the ensemble machine learning model based on user feedback regarding the natural language explanations.

A device comprising:

• a processor;
• and
• a storage medium for tangibly storing thereon program logic for execution by the processor, the program logic comprising steps for:
• receiving data from a plurality of heterogeneous data sources;
• processing each type of received data through a corresponding type-specific processing pipeline by performing temporal pattern extraction specific to each data type;
• generating cross-source feature vectors by performing temporal correlation analysis across the processed data from different data sources;
• training an ensemble machine learning model using the cross-source feature vectors;
• generating engagement predictions and feature contribution values that quantify an impact of each feature on each prediction;
• presenting the engagement predictions with natural language explanations based on the feature contribution values;
• and
• updating weights of the ensemble machine learning model based on user feedback regarding the natural language explanations.