Algorithm for identification of trending content

Abstract

This application relates to techniques for recommending content to a user of a content distribution system. A server device can generate recommendations as part of a user interface for the content distribution system. The server device can be configured to: calculate a trend score for each of a plurality of digital assets managed by a content distribution system, calculate a recommendation score for a subset of digital assets that are not installed on a client device of a target user, calculate a breakout score for a subset of digital assets managed by the content distribution system each having a cumulative number of downloads below a threshold value, rank the digital assets according to the trend scores, the recommendation scores, or the breakout scores, and generate a visual representation of one or more digital assets to recommend to the user based on the ranking.

1 . A method for providing a recommendation for one or more digital assets, the method comprising, at a server device:
 collecting statistical data for the one or more digital assets;   developing a respective score for each digital asset in the one or more digital assets, wherein the respective score comprises a respective breakout score based on a respective number of trendsetters that have downloaded the digital asset from a content distribution system;   generating a visual representation of the one or more digital assets, wherein an order of the digital assets in the visual representation is adjusted based on the score for each digital asset in the one or more digital assets; and   causing at least one client device to display the visual representation of the one or more digital assets.   
     
     
         2 . The method of  claim 1 , wherein, for a given digital asset of the one or more digital assets, the respective score is calculated by:
 identifying a set of digital assets within a particular category of digital assets, each digital asset in the set of digital assets having a corresponding breakout date established for the digital asset;   identifying one or more trendsetters associated with the particular category of digital assets;   generating a list of digital assets within the particular category of digital assets downloaded by at least one trendsetter in the one or more trendsetters;   filtering the list of digital assets downloaded by the at least one trendsetter for the particular category of digital assets to exclude digital assets having a cumulative number of downloads above a threshold value; and   calculating a breakout score for each digital asset in the filtered list of digital assets by counting a number of trendsetters that have downloaded the digital asset.   
     
     
         3 . The method of  claim 2 , wherein a breakout date for a digital asset is identified based on a moving average convergence/divergence (MACD) metric. 
     
     
         4 . The method of  claim 3 , wherein the MACD metric is calculated as an exponentially weighted moving average (EWMA) of a difference between a first time frame EWMA and a second time frame EWMA of historical download data. 
     
     
         5 . The method of  claim 4 , wherein the breakout date is identified when the MACD metric increases above a threshold value. 
     
     
         6 . The method of  claim 1 , wherein the server device is coupled to a database that includes the statistical data for the one or more digital assets. 
     
     
         7 . The method of  claim 1 , further comprising:
 ranking the filtered list of digital assets by their respective breakout scores to identify breakout content to recommend;   generating a second visual representation of at least a subset of the filtered list of digital assets based on the ranking and corresponding breakout dates of the digital assets; and   causing the at least one client device to display the second visual representation.   
     
     
         8 . A non-transitory computer readable storage medium configured to store instructions that, when executed by a processor included in a server device, cause the server device to provide a recommendation for one or more digital assets, by carrying out steps that include:
 collecting statistical data for the one or more digital assets;   developing a respective score for each digital asset in the one or more digital assets, wherein the respective score comprises a respective breakout score based on a respective number of trendsetters that have downloaded the digital asset from a content distribution system;   generating a visual representation of the one or more digital assets, wherein an order of the digital assets in the visual representation is adjusted based on the score for each digital asset in the one or more digital assets; and   causing at least one client device to display the visual representation of the one or more digital assets.   
     
     
         9 . The non-transitory computer readable storage medium of  claim 8 , wherein, for a given digital asset of the one or more digital assets, the respective score is calculated by:
 identifying a set of digital assets within a particular category of digital assets, each digital asset in the set of digital assets having a corresponding breakout date established for the digital asset;   identifying one or more trendsetters associated with the particular category of digital assets;   generating a list of digital assets within the particular category of digital assets downloaded by at least one trendsetter in the one or more trendsetters;   filtering the list of digital assets downloaded by the at least one trendsetter for the particular category of digital assets to exclude digital assets having a cumulative number of downloads above a threshold value; and   calculating a breakout score for each digital asset in the filtered list of digital assets by counting a number of trendsetters that have downloaded the digital asset.   
     
     
         10 . The non-transitory computer readable storage medium of  claim 9 , wherein a breakout date for a digital asset is identified based on a moving average convergence/divergence (MACD) metric. 
     
     
         11 . The non-transitory computer readable storage medium of  claim 10 , wherein the MACD metric is calculated as an exponentially weighted moving average (EWMA) of a difference between a first time frame EWMA and a second time frame EWMA of historical download data. 
     
     
         12 . The non-transitory computer readable storage medium of  claim 11 , wherein the breakout date is identified when the MACD metric increases above a threshold value. 
     
     
         13 . The non-transitory computer readable storage medium of  claim 8 , wherein the server device is coupled to a database that includes the statistical data for the one or more digital assets. 
     
     
         14 . The non-transitory computer readable storage medium of  claim 8 , wherein the steps further include:
 ranking the filtered list of digital assets by their respective breakout scores to identify breakout content to recommend;   generating a second visual representation of at least a subset of the filtered list of digital assets based on the ranking and corresponding breakout dates of the digital assets; and   causing the at least one client device to display the second visual representation.   
     
     
         15 . A server device configured to provide a recommendation for one or more digital assets, the server device comprising a processor configured to cause the server device to carry out steps that include:
 collecting statistical data for the one or more digital assets;   developing a respective score for each digital asset in the one or more digital assets, wherein the respective score comprises a respective breakout score based on a respective number of trendsetters that have downloaded the digital asset from a content distribution system;   generating a visual representation of the one or more digital assets, wherein an order of the digital assets in the visual representation is adjusted based on the score for each digital asset in the one or more digital assets; and   causing at least one client device to display the visual representation of the one or more digital assets.   
     
     
         16 . The server device of  claim 15 , wherein, for a given digital asset of the one or more digital assets, the respective score is calculated by:
 identifying a set of digital assets within a particular category of digital assets, each digital asset in the set of digital assets having a corresponding breakout date established for the digital asset;   identifying one or more trendsetters associated with the particular category of digital assets;   generating a list of digital assets within the particular category of digital assets downloaded by at least one trendsetter in the one or more trendsetters;   filtering the list of digital assets downloaded by the at least one trendsetter for the particular category of digital assets to exclude digital assets having a cumulative number of downloads above a threshold value; and   calculating a breakout score for each digital asset in the filtered list of digital assets by counting a number of trendsetters that have downloaded the digital asset.   
     
     
         17 . The server device of  claim 16 , wherein a breakout date for a digital asset is identified based on a moving average convergence/divergence (MACD) metric. 
     
     
         18 . The server device of  claim 17 , wherein the MACD metric is calculated as an exponentially weighted moving average (EWMA) of a difference between a first time frame EWMA and a second time frame EWMA of historical download data. 
     
     
         19 . The server device of  claim 18 , wherein the breakout date is identified when the MACD metric increases above a threshold value. 
     
     
         20 . The server device of  claim 15 , wherein the server device is coupled to a database that includes the statistical data for the one or more digital assets.