Combined Wide And Deep Machine Learning Models For Automated Database Element Processing Systems, Methods And Apparatuses

A computer system comprising:

• memory hardware configured to store historical feature vector inputs and computer-executable instructions, wherein the historical feature vector inputs include historical article elements and access records associated with the historical article elements;
• and
• processor hardware configured to execute the instructions, wherein the instructions include:
• training a wide machine learning model with the historical feature vector inputs, using unsupervised learning, to generate a wide ranked element output;
• training a deep machine learning model with the historical feature vector inputs, using unsupervised learning, to generate a deep ranked element output;
• generating a set of inputs specific to an individual entity, the set of inputs derived from element access records associated with the individual entity;
• obtaining a set of current article database elements, the current article database elements published according to specified time period criteria;
• creating a feature vector input according to the set of inputs and the set of current article database elements;
• processing, by the wide machine learning model, the feature vector input to retrieve a wide model subset of the current article database elements having a highest correlation with the element access records of the set of inputs;
• processing, by the wide machine learning model, the wide model subset of the current article database elements to generate a wide ranked element list;
• processing, by the deep machine learning model, the feature vector input to retrieve a deep model subset of the current article database elements having a highest correlation with the element access records of the set of inputs;
• processing, by the deep machine learning model, the deep model subset of the current article database elements to generate a deep ranked element list;
• and
• merging database elements of the wide ranked element list and the deep ranked element list to generate a ranked element recommendation output.

A non-transitory computer-readable medium storing process-executable instructions, the instructions comprising:

• training a wide machine learning model with historical feature vector inputs, using unsupervised learning, to generate a wide ranked element output, wherein the historical feature vector inputs include historical article elements and access records associated with the historical article elements;
• training a deep machine learning model with the historical feature vector inputs, using unsupervised learning, to generate a deep ranked element output;
• generating a set of inputs specific to an individual entity, the set of inputs derived from element access records associated with the individual entity;
• obtaining a set of current article database elements, the current article database elements published according to specified time period criteria;
• creating a feature vector input according to the set of inputs and the set of current article database elements;
• processing, by the wide machine learning model, the feature vector input to retrieve a wide model subset of the current article database elements having a highest correlation with the element access records of the set of inputs;
• processing, by the wide machine learning model, the wide model subset of the current article database elements to generate a wide ranked element list;
• processing, by the deep machine learning model, the feature vector input to retrieve a deep model subset of the current article database elements having a highest correlation with the element access records of the set of inputs;
• processing, by the deep machine learning model, the deep model subset of the current article database elements to generate a deep ranked element list;
• and
• merging database elements of the wide ranked element list and the deep ranked element list to generate a ranked element recommendation output.