Rendering a dynamic endemic banner on streaming platforms using content recommendation systems and advanced banner personalization
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, in conjunction with an object recognition model, to enhance dynamic generation of a banner being shown to a user via an awareness or performance campaign. This method allows the platform to present the most relevant ML 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 computer-implemented method comprising:
generating, by at least one computer processor, based on first metadata of a user profile, a call to a content recommendation system powering a streaming media publisher channel for first recommended content assets for a 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 of one or more objects located within imagery of the first recommended content assets, wherein each object of the one or more objects includes identifying second metadata;
retrieving, based on the identifying second metadata, second recommended content assets, wherein the second recommended content assets include at least a portion of third metadata matching the identifying second metadata;
comparing the first metadata and the third metadata;
selecting, based on metadata common to the first metadata and the third metadata, at least one additional object from one or more objects of the second recommended content assets;
extracting the at least one additional object from imagery of the second recommended content assets;
stitching the at least one additional object into the target banner to form a composite banner; and
rendering the composite banner on a display of a media device.
2. The computer-implemented method of claim 1 , wherein the composite banner comprises an endemic banner.
3. The computer-implemented method of claim 1 , wherein the media device comprises an Over-the-Top (OTT) device.
4. The computer-implemented method of claim 1 , further comprising the content recommendation system instantiating the trained machine learning model, based on the first metadata of the user profile, to generate the first recommended content assets.
5. The computer-implemented method of claim 1 , wherein the trained machine learning model comprises an image recognition model.
6. The computer-implemented method of claim 5 , further comprising the image recognition model being trained to recognize any of: faces, themes, genres, scenes, media series, or text within the imagery of the first recommended content assets or the second recommended content assets.
7. The computer-implemented method of claim 1 , further comprising dynamically modifying one or more visual components of the composite banner.
8. The computer-implemented method of claim 7 , wherein the dynamically modifying one or more visual components comprises generating one or more of: artwork, movement, animation, cinemagraphs, resizing, scaling, cropping, image framing, color changes, font changes, or composite filling.
9. The computer-implemented method of claim 1 , wherein a closest match comprises one or more of: the first metadata being identical to the third metadata, or at least a portion of the first metadata being identical to the portion of the third metadata.
10. A system, comprising:
one or more memories;
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 call to a content recommendation system powering a streaming media publisher channel for first recommended content assets for a 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 of one or more objects located within imagery of the first recommended content assets, wherein each object of the one or more objects includes identifying second metadata;
retrieving, based on the identifying second metadata, second recommended content assets, wherein the second recommended content assets include at least a portion of third metadata matching the identifying second metadata;
comparing the first metadata and the third metadata;
selecting, based on metadata common to the first metadata and the third metadata, at least one additional object from one or more objects of the second recommended content assets;
extracting the at least one additional object from imagery of the second recommended content assets;
stitching the at least one additional object into the target banner to form a composite banner; and
rendering the composite banner on a display of a media device.
11. The system of claim 10 , where the composite banner comprises an endemic banner.
12. The system of claim 10 , where the system comprises a streaming media device platform for an Over-the-Top (OTT) device.
13. The system of claim 10 , the operations further comprising instantiating the trained machine learning model, based on the first metadata of the user profile, to generate the first recommended content assets.
14. The system of claim 10 , wherein the trained machine learning model comprises an image recognition model.
15. The system of claim 14 , the operations further comprising training the image recognition model to recognize any of: faces of actors, characters, or text within the imagery of the first recommended content assets or the second recommended content assets.
16. The system of claim 14 , the operations further comprising training the image recognition model to recognize any of: themes, genres, scenes, or a media series within the imagery of the first recommended content assets or the second recommended content assets.
17. The system of claim 10 , the operations further comprising dynamically modifying one or more visual components of the composite banner by generating one or more of: artwork, movement, animation, cinemagraphs, resizing, scaling, cropping, image framing, color changes, font changes, or composite filling.
18. 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 call to a content recommendation system powering a streaming media publisher channel for first recommended content assets for a 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 of one or more objects located within imagery of the first recommended content assets, wherein each object of the one or more objects includes identifying second metadata;
retrieving, based on the identifying second metadata, second recommended content assets, wherein the second recommended content assets include at least a portion of third metadata matching the identifying second metadata;
comparing the first metadata and the third metadata;
selecting, based on metadata common to the first metadata and the third metadata, at least one additional object from one or more objects of the second recommended content assets;
extracting the at least one additional object from imagery of the second recommended content assets;
stitching the at least one additional object into the target banner to form a composite banner; and
rendering the composite banner on a display of a media device.
19. The non-transitory computer-readable medium of claim 18 , the operations further comprising dynamically modifying one or more visual components of the composite banner by generating one or more of: artwork, movement, animation, cinemagraphs, resizing, scaling, cropping, image framing, color changes, font changes, or composite filling.
20. The non-transitory computer-readable medium of claim 18 , the operations further comprising the content recommendation system instantiating the trained machine learning model, based on the first metadata of the user profile, to generate the first recommended content assets.