Crowdsourcing platform for on-demand media content creation and sharing
This disclosure describes techniques for improving user experience when servicing on-demand requests by a consumer for a hyper-specific media content in a network environment. The hyper-specific media content may include specified audio, video, etc. that relates to hyper-specific criteria (e.g., specific subject, location, and time condition). In an embodiment, the hyper-specific criteria in a consumer request are compared with stored hyper-specific criteria in a database. A prediction model is then trained on feature parameters of matching stored hyper-specific criteria to generate a trending level classification that can be associated with the hyper-specific criteria in the consumer request. The trending level classification indicates level of interest of third parties and may be used as a basis for content management.
1. One or more computer-readable storage media collectively storing computer-executable instructions that upon execution collectively cause one or more computers to perform acts comprising:
receiving, from a first device, a request for a real-time delivery of a media content of a particular future event, wherein the request includes a hyper-specific media content criteria having a specific subject, location, and a time for the first device to order exactly the media content of the particular future event that will be authored by a second device;
comparing the specific subject and the specific location with stored specific subjects and locations in a database;
updating a classification that is associated with matching specific subject and location, wherein the updating includes training an algorithm with respect to at least one feature of the matching specific subject and location; and
performing a media content management based upon the classification.
2. The one or more computer-readable storage media of claim 1 , wherein the classification includes a trending level classification that indicates a measure of interest for the particular future event.
3. The one or more computer-readable storage media of claim 1 , wherein the at least one feature includes number of downloads and number of views of a media content that is associated with the matching specific subject and location.
4. The one or more computer-readable storage media of claim 1 , wherein the at least one feature includes parameters that are monitored over a particular time period.
5. The one or more computer-readable storage media of claim 1 , wherein the algorithm is trained periodically to update corresponding classifications of the stored specific subjects and locations.
6. The one or more computer-readable storage media of claim 1 , wherein the stored specific subjects and locations are associated with previously transmitted media contents.
7. The one or more computer-readable storage media of claim 1 , wherein the algorithm includes a Random Forest algorithm.
8. The one or more computer-readable storage media of claim 1 , wherein the algorithm is created from a training data that includes manually classified stored specific subjects and locations.
9. The one or more computer-readable storage media of claim 1 , wherein an updated classification that is associated with the matching specific subject and location is associated with the hyper-specific media content criteria in a received request.
10. A device, comprising:
a processor;
a memory coupled to the processor, the memory storing computer-executable instructions that are executable by the processor to perform a plurality of actions, the plurality of actions comprising:
receiving a request for a real-time delivery of a media content of a particular future event, wherein the request includes a hyper-specific media content criteria having a specific subject, location, and a time for the request to or order exactly the media content of the particular future event that will be authored by another device;
comparing the specific subject and the specific location with stored specific subjects and locations in a database; and
training an algorithm with respect to at least one feature of matching specific subject and location to update a classification that is associated with the matching specific subject and location; and
facilitating media content management based upon the classification.
11. The device of claim 10 , wherein the classification includes a trending level classification that indicates a measure of interest for the particular future event.
12. The device of claim 10 , wherein the at least one feature includes number of downloads and number of views of a media content that is associated with the matching specific subject and location.
13. The device of claim 10 , wherein the at least one feature includes parameters that are monitored over a particular time period.
14. The device of claim 10 , wherein the algorithm is trained periodically to update corresponding classifications of the stored specific subjects and locations.
15. The device of claim 10 , wherein the stored specific subjects and locations are associated with previously transmitted media contents.
16. The device of claim 10 , wherein the algorithm includes a Random Forest algorithm.
17. The device of claim 10 , wherein the algorithm is created from a training data that includes manually classified stored specific subjects and locations.
18. A computer-implemented method, comprising:
receiving, from a first device, a request for a real-time delivery of a media content of a particular future event, wherein the request includes a hyper-specific media content criteria having a specific subject, location, and a time for the first device to order exactly the media content of the particular future event that will be authored by a second device;
comparing the specific media content subject and media content location with stored specific subjects and locations in a database;
periodically training an algorithm with respect to at least one feature of matching specific subject and location to update a classification that is associated with the matching specific subject and location;
sending the media content including the classification to the first device; and
performing a media content management based upon the classification.
19. The computer-implemented method of claim 18 , wherein the classification includes a trending level classification that indicates a measure of interest for the particular future event.
20. The computer-implemented method of claim 18 , wherein the at least one feature includes number of downloads and number of views of a media content that is associated with the matching specific subject and location.