Rendering a dynamic endemic banner on streaming platforms using content recommendation systems and content modeling for user exploration and awareness
Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for utilizing a content recommendation system powering a streaming media publisher channel to enhance an ad creative being shown to the user via awareness or performance campaigns. This method allows the platform to present the most relevant exploratory personalized in-channel content to the publisher platform users in endemic banners that run on the platform which then correspondingly helps drive user reach. An example embodiment operates by implementing personalized content banners that may act as a hook for channel users opening their streaming device, both active and lapsed, to enter back into the channel.
1. A computer implemented method for creating dynamic banners, the method 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 differ 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, at a media device, 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 the media device.
2. The method of claim 1 , wherein the composite banner comprises an endemic banner.
3. The method of claim 1 , wherein the media device comprises an Over-the-Top (OTT) device.
4. The method of claim 1 , wherein the generating, based on the trained machine learning model, the first call to the content recommendation system for the first subset of content assets for the target banner template comprises matching interests of a user to the content assets.
5. The method of claim 4 , wherein the first subset of content assets comprises content assets recommended based on the interests of the user and the trained machine learning model further comprises an exploratory model trained to select content assets not in the first subset of content assets.
6. The method of claim 1 , wherein the at least the second subset of content assets comprises at least the second subset of content assets and a third subset of content assets, wherein the at least the second subset of content assets comprises content assets matched to related interests of a user and not part of the first subset of content assets, and the third subset of content assets comprises random content assets that are not part of the first or second subsets of content assets.
7. The method of claim 1 , wherein the target banner template is selected based on any of:
opening an application (App);
executing a first-time view;
subscribing to a service;
resumption of watching targeted content;
completion of watching targeted content; or
completion of watching a sponsorship program.
8. 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.
9. The system of claim 8 , where the composite banner comprises an endemic banner.
10. The system of claim 8 , where the system comprises a streaming media device platform for an Over-the-Top (OTT) device.
11. The system of claim 8 , wherein the generating, based on the trained machine learning model, the first call to the content recommendation system for the first subset of content assets for the target banner template further comprises matching interests of a user to the content assets.
12. The system of claim 11 , wherein the first subset of content assets comprises content assets recommended based on the interests of the user and the trained machine learning model further comprises an exploratory model trained to select content assets not in the first subset of content assets.
13. The system of claim 8 , wherein the at least the second subset of content assets comprises a second subset of content assets matched to related interests of a user and not part of the first subset of content assets and further comprises a third subset of content assets of random content assets that are not part of the first or second subsets of content assets.
14. 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.
15. The non-transitory computer-readable medium of claim 14 , wherein the composite banner comprises an endemic banner and the at least one computing device comprises an Over-the-Top (OTT) device.
16. The non-transitory computer-readable medium of claim 14 , wherein the at least the second subset of content assets comprises a second subset of content assets matched to related interests of a user and not part of the first subset of content assets and further comprises a third subset of content assets of random content assets that are not part of the first or second subsets of content assets.