Systems and methods for real time configurable recommendation using user data

A system (100) comprising:

• one or more data storage devices (102) operatively coupled to one or more hardware processors (104) and configured to store instructions configured for execution by the one or more hardware processors to:
• receive, via a batch interface, raw user data associated with a plurality of users from a plurality of sources;
• merge the raw user data into a Common Data Format (CDF), wherein the CDF is a single file with records in the raw user data sorted on timestamp values associated thereof and viewed as a star schema of a fact table joined with dimension tables wherein the fact table pertains to user actions captured in the raw user data and the dimension tables are descriptive data of columns in the fact table;
• and
• generate a configurable recommendation model by:
• processing the CDF to generate a set of features for building one or more machine learning models and one or more deep learning models, wherein the features comprise temporal features and non-temporal features;
• creating a feature dictionary for the one or more machine learning models, wherein the feature dictionary is an in-memory persistent store configured to store the set of features and values associated thereof;
• and
• ensembling the one or more machine learning models and the one or more deep learning models built using the generated set of features, to generate the configurable recommendation model.

A computer program product comprising a non-transitory computer readable medium having a computer readable program embodied therein, wherein the computer readable program, when executed on a computing device, causes the computing device to:

• receive, via a batch interface, raw user data associated with a plurality of users from a plurality of sources;
• merge the raw user data into a Common Data Format (CDF), wherein the CDF is a single file with records in the raw user data sorted on timestamp values associated thereof and viewed as a star schema of a fact table joined with dimension tables wherein the fact table pertains to user actions captured in the raw user data and the dimension tables are descriptive data of columns in the fact table;
• and
• generate a configurable recommendation model by:
• processing the CDF to generate a set of features for building one or more machine learning models and one or more deep learning models, wherein the features comprise temporal features and non-temporal features;
• creating a feature dictionary for the one or more machine learning models, wherein the feature dictionary is an in-memory persistent store configured to store the set of features and values associated thereof;
• and
• ensembling the one or more machine learning models and the one or more deep learning models built using the generated set of features, to generate the configurable recommendation model.