Rendering a dynamic endemic banner on streaming platforms using content recommendation systems and content modeling for user exploration and awareness

A system, comprising:

• a memory;
• and
• at least one processor coupled to the memory and configured to perform operations comprising:
• generating, based on a trained machine learning model, a first call to a content recommendation system for a first subset of content assets for a target banner template;
• generating, based on the trained machine learning model and at least a second subset of content assets, a second call to the content recommendation system for recommended content assets for the target banner template, wherein the second subset of content assets differs from the first subset of content assets;
• selecting, based on the trained machine learning model, a content asset from the first subset of content assets or the at least the second subset of content assets, wherein the selecting of the content asset from the at least the second subset of content assets is based on the trained machine learning model being trained to weight content asset subset generation based on the at least the second subset of content assets being selected when a composite banner is historically selected by users at a frequency above a threshold value;
• receiving, from the content recommendation system, the content asset;
• stitching the content asset into the target banner template to form the composite banner;
• and
• rendering the composite banner on a display of a media device.

A non-transitory computer-readable medium having instructions stored thereon that, when executed by at least one computing device, cause the at least one computing device to perform operations comprising:

• generating, based on a trained machine learning model, a first call to a content recommendation system for a first subset of content assets for a target banner template, wherein the second subset of content assets differ from the first subset of content assets;
• generating, based on the trained machine learning model and at least a second subset of content assets, a second call to the content recommendation system for recommended content assets for the target banner template;
• selecting, based on the trained machine learning model, a content asset from the first subset of contents or the at least the second subset of content assets, wherein the selecting of the content asset from the at least the second subset of content assets is based on the trained machine learning model being trained to weight content asset subset generation based on the at least the second subset of content assets being selected when a composite banner is historically selected by users at a frequency above a threshold value;
• receiving, from the content recommendation system, the content asset;
• stitching the content asset into the target banner template to form the composite banner;
• and
• rendering the composite banner on a display of a media device.