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, wherein the first subset of content assets includes a first number of content selections reflecting a user's interests;
• generating, based on the trained machine learning model, a second call to the content recommendation system for a second subset of content assets for the target banner template, wherein the second subset of content assets includes a second number of content selections related to the user's interests and differs from the first subset of content assets, wherein the trained machine learning model is trained to weight content asset subset generation based on the target banner template, including at least the second subset of the content assets being selected by the user at a frequency above a threshold value;
• generating, based on the trained machine learning model, a third call to the content recommendation system for a third subset of content assets for the target banner template, wherein the third subset of the content assets includes a third number of content selections unrelated to the user's interests and absent from either the first subset or the second subset of content assets;
• determining, based on the first number, the second number, and the third number, a collective number of the first subset of content assets, the second subset of content assets, and the third subset of content assets;
• selecting, based on an exploration strategy, a specific content asset from the collective number of the content assets, wherein the exploration strategy selects the specific content asset based on a frequency of occurrence of each of the first number, the second number, and the third number relative to the collective number;
• receiving, at a media device, the content asset;
• stitching the specific content asset into the target banner template to form a composite banner;
• and
• rendering the composite banner on a display of the 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 first subset of content assets includes a first number of content selections reflecting a user's interests;
• generating, based on the trained machine learning model, a second call to the content recommendation system for a second subset of content assets for the target banner template, wherein the second subset of content assets includes a second number of content selections related to the user's interests and differs from the first subset of content assets, wherein the trained machine learning model is trained to weight content asset subset generation based on the target banner template, including at least the second subset of the content assets being selected by the user at a frequency above a threshold value;
• generating, based on the trained machine learning model, a third call to the content recommendation system for a third subset of content assets for the target banner template, wherein the third subset of the content assets includes a third number of content selections unrelated to the user's interests and absent from either the first subset or the second subset of content assets;
• determining, based on the first number, the second number, and the third number, a collective number of the first subset of content assets, the second subset of content assets, and the third subset of content assets;
• selecting, based on an exploration strategy, a specific content asset from the collective number of the content assets, wherein the exploration strategy selects the specific content asset based on a frequency of occurrence of each of the first number, the second number, and the third number relative to the collective number;
• receiving, at a media device, the content asset;
• stitching the specific content asset into the target banner template to form a composite banner;
• and
• rendering the composite banner on a display of the media device.