Systems and methods for ranking and providing related media content based on signals

Abstract

Systems, methods, and non-transitory computer-readable media can detect a trigger to generate a set of media content items associated with at least one of a particular media content item or a user viewing the particular media content item. A plurality of content generators can be utilized to generate a plurality of subsets of media content items. Each of the plurality of content generators can identify a respective subset out of the plurality of subsets of media content items based on at least one of information associated with the particular media content item or information associated with the user viewing the particular media content item. At least some media content items in at least some of the plurality of subsets of media content items can be ranked based on respective information associated with each media content item.

1 - 20 . (canceled) 
     
     
         21 . A computer-implemented method comprising:
 utilizing, by a computing system, each content generator of a plurality of content generators to generate, respectively, a subset of media content items of a plurality of subsets of media content items based on information relating to a particular media content item associated with a media channel;   ranking, by the computing system, media content items of the plurality of subsets of media content items based on media channel data associated with the media channel;   selecting, by the computing system, one or more media content items from the media content items based on satisfaction of ranking criteria, the one or more media content items included in a set of media content items; and   presenting, by the computing system, the set of media content items.   
     
     
         22 . The computer-implemented method of  claim 21 , wherein the plurality of subsets of media content items are generated in response to detection of a trigger. 
     
     
         23 . The computer-implemented method of  claim 21 , wherein the ranking comprises:
 acquiring the media channel data;   generating one or more signals based on the media channel data; and   training a predictive model based on machine learning using the one or more signals.   
     
     
         24 . The computer-implemented method of  claim 23 , wherein the ranking further comprises:
 predicting, based on the predictive model, one or more respective events for each of the media content items;   combining the one or more respective events for each of the media content items to produce a respective event metric for each of the media content items; and   sorting the media content items based on the respective event metric for each of the media content items.   
     
     
         25 . The computer-implemented method of  claim 24 , wherein the one or more respective events for each of the media content items includes at least one of a like event, a share event, a comment event, or a view duration event. 
     
     
         26 . The computer-implemented method of  claim 21 , wherein the plurality of content generators includes at least one of a behavioral similarity generator, a content similarity generator, a crowd sourcing generator, a same source generator, a user targeting generator, or a trending topic generator. 
     
     
         27 . The computer-implemented method of  claim 26 , wherein the behavioral similarity generator utilizes collaborative filtering to identify a particular subset of media content items of the plurality of subsets of media content items, and wherein the collaborative filtering is based on at least one of a like signal, a share signal, a comment signal, a view duration signal, or a mention signal. 
     
     
         28 . The computer-implemented method of  claim 26 , wherein the content similarity generator identifies a particular subset of media content items of the plurality of subsets of media content items, and wherein the particular subset of media content items is identified by the content similarity generator based on at least one of a mention signal, a text signal, a tag signal, a topic classification signal, or an image classification signal. 
     
     
         29 . The computer-implemented method of  claim 26 , wherein the user targeting generator identifies a particular subset of media content items of the plurality of subsets of media content items, wherein the particular subset of media content items is identified by the user targeting generator based on the information associated with a user, and wherein the information associated with the user includes at least one of user usage pattern data, user viewing history data, or user demographic data. 
     
     
         30 . The computer-implemented method of  claim 21 , wherein the set of media content items includes a set of one or more videos, and wherein the particular media content item includes a particular video within a particular video channel. 
     
     
         31 . A system comprising:
 at least one processor; and   a memory storing instructions that, when executed by the at least one processor, cause the system to perform:   utilizing each content generator of a plurality of content generators to generate, respectively, a subset of media content items of a plurality of subsets of media content items based on information relating to a particular media content item associated with a media channel;   ranking media content items of the plurality of subsets of media content items based on media channel data associated with the media channel;   selecting one or more media content items from the media content items based on satisfaction of ranking criteria, the one or more media content items included in a set of media content items; and   presenting the set of media content items.   
     
     
         32 . The system of  claim 31 , wherein the plurality of subsets of media content items are generated in response to detection of a trigger. 
     
     
         33 . The system of  claim 31 , wherein the ranking comprises:
 acquiring the media channel data;   generating one or more signals based on the media channel data; and   training a predictive model based on machine learning using the one or more signals.   
     
     
         34 . The system of  claim 33 , wherein the ranking further comprises:
 predicting, based on the predictive model, one or more respective events for each of the media content items;   combining the one or more respective events for each of the media content items to produce a respective event metric for each of the media content items; and   sorting the media content items based on the respective event metric for each of the media content items.   
     
     
         35 . The system of  claim 34 , wherein the one or more respective events for each of the media content items includes at least one of a like event, a share event, a comment event, or a view duration event. 
     
     
         36 . A non-transitory computer-readable storage medium including instructions that, when executed by at least one processor of a computing system, cause the computing system to perform a method comprising:
 utilizing each content generator of a plurality of content generators to generate, respectively, a subset of media content items of a plurality of subsets of media content items based on information relating to a particular media content item associated with a media channel;   ranking media content items of the plurality of subsets of media content items based on media channel data associated with the media channel;   selecting one or more media content items from the media content items based on satisfaction of ranking criteria, the one or more media content items included in a set of media content items; and   presenting the set of media content items.   
     
     
         37 . The non-transitory computer-readable storage medium of  claim 36 , wherein the plurality of subsets of media content items are generated in response to detection of a trigger. 
     
     
         38 . The non-transitory computer-readable storage medium of  claim 36 , wherein the ranking comprises:
 acquiring the media channel data;   generating one or more signals based on the media channel data; and   training a predictive model based on machine learning using the one or more signals.   
     
     
         39 . The non-transitory computer-readable storage medium of  claim 38 , wherein the ranking further comprises:
 predicting, based on the predictive model, one or more respective events for each of the media content items;   combining the one or more respective events for each of the media content items to produce a respective event metric for each of the media content items; and   sorting the media content items based on the respective event metric for each of the media content items.   
     
     
         40 . The non-transitory computer-readable storage medium of  claim 39 , wherein the one or more respective events for each of the media content items includes at least one of a like event, a share event, a comment event, or a view duration event.