Digital content curation and distribution system and method
Disclosed herein are systems and method for curating and distributing digital content; including: digital video, music, pictures, etc. For example, presented herein are systems and methods for providing a digital content program, including a plurality of continuously provided digital assets streamed over a digital content platform. Example embodiments include: (a) curating a plurality of assets; (b) selecting a subset of assets from the plurality of assets, based on similarity metrics between assets; and (c) ordering the subset of assets into a digital content program.
1. A method, comprising:
creating a directed weighted content graph by defining relationships between a plurality of video assets;
selecting a subset of video assets from the plurality of video assets, based on a similarity measurement between a user-selected video asset and one or more of the plurality of video assets using the directed weighted content graph, the subset of video assets includes two or more video assets;
creating a digital video content program containing the subset of video assets, the subset of video assets ordered within the digital video content program based on at least a diversity amongst the subset of video assets;
sending the digital video content program to a user device.
2. The method of claim 1 , wherein the similarity measurement includes a calculation of minimum graph distance between the user-selected video asset and one or more video assets in the content graph.
3. The method of claim 1 , wherein the ordering of the subset of video assets is further based in part on a current user's interaction with the user-selected video asset.
4. The method of claim 1 , wherein the ordering of the subset of video assets is further based in part on user interaction with one or more assets in the content graph.
5. The method of claim 1 , wherein the ordering of the subset of video assets is further based in part on aggregate user data.
6. The method of claim 1 , wherein the ordering of the subset of video assets is further based in part on one or more of: system data, computational capabilities, or bit rate delivery.
7. The method of claim 1 , wherein the ordering of the subset of video assets is further based in part on human-curation inputs.
8. The method of claim 1 , wherein the ordering of the subset of video assets is further based in part on machine-learned curation inputs.
9. The method of claim 1 , wherein the ordering of the subset of video assets is further based in part on quality of each video asset in the subset of video assets.
10. The method of claim 1 , further comprising:
reordering the subset of video assets in the digital video content program based on one or more of: asset data, end-user data, or computational capabilities.
11. The method of claim 1 , further comprising:
reordering the subset of video assets in the digital video content program based on one or more of: asset data, end-user data, or computational capabilities;
sending the digital video content program including the reordered subset of video assets to the user device.
12. One or more non-transitory computer-readable storage media, storing one or more sequences of instructions, which when executed by one or more processors cause performance of:
creating a directed weighted content graph by defining relationships between a plurality of video assets;
selecting a subset of video assets from the plurality of video assets, based on a similarity measurement between a user-selected video asset and one or more of the plurality of video assets using the directed weighted content graph, the subset of video assets includes two or more video assets;
creating a digital video content program containing the subset of video assets, the subset of video assets ordered within the digital video content program based on at least a diversity amongst the subset of video assets;
sending the digital video content program to a user device.
13. The one or more non-transitory computer-readable storage media of claim 12 , wherein the similarity measurement includes a calculation of minimum graph distance between the user-selected video asset and one or more video assets in the content graph.
14. The one or more non-transitory computer-readable storage media of claim 12 , wherein the ordering of the subset of video assets is further based in part on a current user's interaction with the user-selected video asset.
15. The one or more non-transitory computer-readable storage media of claim 12 , wherein the ordering of the subset of video assets is further based in part on user interaction with one or more assets in the content graph.
16. The one or more non-transitory computer-readable storage media of claim 12 , wherein the ordering of the subset of video assets is further based in part on aggregate user data.
17. The one or more non-transitory computer-readable storage media of claim 12 , wherein the ordering of the subset of video assets is further based in part on one or more of: system data, computational capabilities, or bit rate delivery.
18. The one or more non-transitory computer-readable storage media of claim 12 , wherein the ordering of the subset of video assets is further based in part on human-curation inputs.
19. The one or more non-transitory computer-readable storage media of claim 12 , wherein the ordering of the subset of video assets is further based in part on machine-learned curation inputs.
20. The one or more non-transitory computer-readable storage media of claim 12 , wherein the ordering of the subset of video assets is further based in part on quality of each video asset in the subset of video assets.
21. The one or more non-transitory computer-readable storage media of claim 12 , further comprising:
reordering the subset of video assets in the digital video content program based on one or more of: asset data, end-user data, or computational capabilities.
22. The one or more non-transitory computer-readable storage media of claim 12 , further comprising:
reordering the subset of video assets in the digital video content program based on one or more of: asset data, end-user data, or computational capabilities;
sending the digital video content program including the reordered subset of video assets to the user device.
23. A system, comprising:
a subsystem, implemented at least partially in hardware, configured to create a directed weighted content graph by defining relationships between a plurality of video assets;
a subsystem, implemented at least partially in hardware, configured to select a subset of video assets from the plurality of video assets, based on a similarity measurement between a user-selected video asset and one or more of the plurality of video assets using the directed weighted content graph, the subset of video assets includes two or more video assets;
a subsystem, implemented at least partially in hardware, configured to create a digital video content program containing the subset of video assets, the subset of video assets ordered within the digital video content program based on at least a diversity amongst the subset of video assets;
a subsystem, implemented at least partially in hardware, configured to send the digital video content program to a user device.
24. The system of claim 23 , wherein the similarity measurement includes a calculation of minimum graph distance between the user-selected video asset and one or more video assets in the content graph.
25. The system of claim 23 , wherein the ordering of the subset of video assets is further based in part on a current user's interaction with the user-selected video asset.
26. The system of claim 23 , wherein the ordering of the subset of video assets is further based in part on user interaction with one or more assets in the content graph.
27. The system of claim 23 , wherein the ordering of the subset of video assets is further based in part on aggregate user data.
28. The system of claim 23 , wherein the ordering of the subset of video assets is further based in part on one or more of: system data, computational capabilities, or bit rate delivery.
29. The system of claim 23 , wherein the ordering of the subset of video assets is further based in part on human-curation inputs.
30. The system of claim 23 , wherein the ordering of the subset of video assets is further based in part on machine-learned curation inputs.
31. The system of claim 23 , wherein the ordering of the subset of video assets is further based in part on quality of each video asset in the subset of video assets.
32. The system of claim 23 , further comprising:
a subsystem, implemented at least partially in hardware, configured to reorder the subset of video assets in the digital video content program based on one or more of: asset data, end-user data, or computational capabilities.
33. The system of claim 23 , further comprising:
a subsystem, implemented at least partially in hardware, configured to reorder the subset of video assets in the digital video content program based on one or more of: asset data, end-user data, or computational capabilities;
wherein the send subsystem sends the digital video content program including the reordered subset of video assets to the user device.