Rendering a dynamic endemic banner on streaming platforms using content recommendation systems and advanced banner personalization

A system, comprising:

• one or more memories;
• and
• at least one processor each coupled to at least one of the memories and configured to perform operations comprising:
• generating, based on first metadata of a user profile, a first call to a content recommendation system powering a streaming media publisher channel for a first recommended content asset for a first target banner, wherein the first metadata includes a user's preferences for content assets;
• initiating, based on a trained machine learning model, an object recognition algorithm to identify a plurality of objects located within imagery of the first recommended content asset, wherein each object of the plurality of objects includes identifying second metadata;
• comparing the first metadata and the second metadata;
• selecting, based on metadata common to the first metadata and the second metadata, at least one object from the plurality of objects;
• extracting the at least one object from the imagery of the first recommended content asset;
• stitching the at least one object into the first target banner to form a first composite banner;
• generating, based on the second metadata, a second call to the content recommendation system for a second recommended content asset;
• stitching imagery of the second recommended content asset into a second target banner to form a second composite banner;
• and
• rendering the first and second composite banners 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 first metadata of a user profile, a first call to a content recommendation system powering a streaming media publisher channel for a first recommended content asset for a first target banner, wherein the first metadata includes a user's preferences for content assets;
• initiating, based on a trained machine learning model, an object recognition algorithm to identify a plurality of objects located within imagery of the first recommended content asset, wherein each object of the plurality of objects includes identifying second metadata;
• comparing the first metadata and the second metadata;
• selecting, based on metadata common to the first metadata and the second metadata, at least one object from the plurality of objects;
• extracting the at least one object from the imagery of the first recommended content asset;
• stitching the at least one object into the first target banner to form a first composite banner;
• generating, based on the second metadata, a second call to the content recommendation system for a second recommended content asset;
• stitching imagery of the second recommended content asset into a second target banner to form a second composite banner;
• and
• rendering the first and second composite banners on a display of a media device.