Content for targeted transmission

Abstract

Machine readable instructions for providing targeted advertisements for a piece of digital content receive a set of user metadata for a group of users. The machine readable instructions select at least one user from the group of users. In addition, the machine readable instructions select a term from a category of terms related to the piece of digital content. Moreover, the machine readable instructions determine a first value corresponding to the at least one user. Also, the machine readable instructions determine a second for the group of users. In addition, the machine readable instructions determine a user score based at least in part on the first value and the second value. When the user score is within a particular range, the machine readable instructions provide an advertisement for the piece of digital content to an electronic device associated with the at least one user.

1 . A tangible, non-transitory machine readable medium comprising machine readable instructions for providing targeted advertisements for a piece of digital content, when executed by one or more processors cause the one or more processors to:
 receive a set of user metadata for a group of users, the metadata comprising data relating to user browsing history, purchase history, term usage history, social media posts and actions, location information, or a combination thereof;   identify a subset of users of the group of users, the subset of users share a common attribute found in the set of user metadata;   select at least one user from the subset of users;   select a term from a category of terms related to the piece of digital content;   determine a first value by comparing a first total of appearances of the term and a second total of appearances of all terms from the category of terms within the set of user metadata corresponding to the at least one user;   determine a second value for the subset of users by determining a portion of users of the subset of users for which the term appears in the set of user metadata corresponding to the subset of users;   determine a user score based at least in part on the first value and the second value; and   when the user score is within a particular predetermined range, provide an advertisement for the piece of digital content to one or more electronic devices associated with the at least one user.   
     
     
         2 . The machine readable medium of  claim 1 , wherein each appearance of the first total of appearances and the second total of appearances is multiplied by a weight value, and the weight value is based at least in part on a type of the appearance, an age of the appearance, or both. 
     
     
         3 . The machine readable medium of  claim 1 , wherein the common attribute comprises a first range of total of purchased movie tickets, a second range of total number of terms in the set of user metadata corresponding to each user of the subset of users, or both. 
     
     
         4 . The machine readable medium of  claim 3 , wherein selecting the at least one user comprises selecting a user with an associated number of purchased movie tickets that is within the first range. 
     
     
         5 . The machine readable medium of  claim 3 , wherein selecting the at least one user comprises selecting a user with a third total number of terms that is within the second range. 
     
     
         6 . The machine readable medium of  claim 1 , comprising machine readable instructions to normalize the user score to produce a normalized user score having a value between zero and one hundred. 
     
     
         7 . The machine readable medium of  claim 1 , wherein providing the advertisement based at least in part on the user score comprises providing the advertisement if the user score is below an upper threshold value and above a lower threshold value. 
     
     
         8 . The machine readable medium of  claim 1 , wherein the term category comprises at least one of a director, genre, actor, or studio. 
     
     
         9 . A method for providing secondary content, related to primary content, for targeted transmission, comprising:
 receiving a set of user metadata for a group of users, the metadata comprising one or more of user browsing history, purchase history, term usage history, social media posts and actions, or location information;   identifying a subset of the group of users, the subset sharing a common attribute;   selecting a user from the subset of the group of users;   selecting a term associated with the primary content from a category of related terms;   determining a first value for the selected term based on a first total number of occurrences of the term for the user and a total number of terms within the category of related terms for the user;   determining a second value associated with the selected term based on a number of users within the subset of users and a second total number of occurrences of the term for all users within the subset of the users;   determining a user score for the selected user based on the determined first value and the determined second value; and   providing the secondary content to the user based on the determined user score.   
     
     
         10 . The method of  claim 9 , wherein each appearance of the first total number of occurrences and the total number of terms is multiplied by a weight value, and the weight value is based at least in part on a type of the appearance, an age of the appearance, or both. 
     
     
         11 . The method of  claim 9 , comprising determining the set of users based at least in part on a first range of total of purchased movie tickets, a second range of total number of terms in the set of user metadata corresponding to each user of the set of users, or both. 
     
     
         12 . The method of  claim 11 , wherein a third total of purchased movie tickets for the at least one user is within the first range. 
     
     
         13 . The method of  claim 11 , wherein a fourth total number of terms corresponding to at the least one user is within the second range. 
     
     
         14 . The method of  claim 9 , comprising:
 selecting a second set of terms associated with the primary content from second categories of related terms;   determining third values for the selected second set of terms based on a second total number of occurrences of the second set of terms for the user and a total number of terms within the second categories of related terms for the user;   determining fourth values that are associated with the selected second set of terms based on a second number of users within the subset of users and a total number of occurrences of the second set of terms for all users within the subset of the users;   determining the user score for the selected user based further upon the third values and the fourth values.   
     
     
         15 . The method of  claim 14 , comprising aggregating results of the third values and the fourth values with the determined first value and the determined second value to determine the user score. 
     
     
         16 . The method of  claim 9 , wherein providing the secondary content based at least in part on the user score comprises providing an advertisement if the user score is below an upper threshold value and above a lower threshold value. 
     
     
         17 . A cloud services system, comprising:
 prediction logic, configured to:
 receive a set of user metadata for a group of users, the metadata comprising data relating to user browsing history, purchase history, term usage history, social media posts and actions, location information, or a combination thereof; 
 identify a subset of users of the group of users, the subset of users share a common attribute found in the set of user metadata; 
 select at least one user from the subset of users; 
 select a term from a category of terms related to the piece of digital content; 
 determine a first value by comparing a first total of appearances of the term and a second total of appearances of all terms from the category of terms within the set of user metadata corresponding to the at least one user; 
 determine a second value for the subset of users by determining a portion of users of the subset of users for which the term appears in the set of user metadata corresponding to the subset of users; and 
 determine an affinity user score based at least in part on the first value and the second value; 
   
       machine learning logic, configured to:
 identify correlations between a desired behavior and different feature combinations; 
 convert the set of user metadata into a set of features; and 
 determine a machine-learning user score based at least in part on a comparison between the different feature combinations and the set of features; and 
 
       presentation logic, configured to provide targeted content associated with the piece of digital content to one or more electronic devices associated with the at least one user an when the affinity user score, the machine-learning user score, or both are within respective predetermined ranges. 
     
     
         18 . The cloud services system of  claim 17 , wherein the presentation logic is configured to provide the targeted content when the affinity user score, the machine-learning user score, or both are below respective upper threshold values and above respective lower threshold values. 
     
     
         19 . The cloud services system of  claim 17 , wherein the prediction logic is configured to determine the subset of users based at least in part on a first range of total interactions with content, a second range of total number of terms in the set of user metadata corresponding to each user of the subset of users, or both. 
     
     
         20 . The cloud services system of  claim 17 , wherein each appearance of the first total of appearances and the second total of appearances is multiplied by a weight value, and the weight value is based at least in part on a type of the appearance, an age of the appearance, or both. 
     
     
         21 . A tangible, non-transitory, machine-readable medium, comprising machine-readable instructions that, when executed by one or more processors, cause the one or more processors to:
 generate a set scores for a customer base, by:
 receiving a set of metadata for a pre-selected set of movies having associated metadata overlapping associated metadata of a pre-determined primary piece of content; 
 receiving a set of scoring affinities that provide an indication of a set of characteristics that a set of customers have an affinity for, do not have an affinity for, or both; 
 receiving a set of weights, generated from a predicative model, the set of weights, based upon a predicted impact that one or more of the set of characteristics has on a target interaction with the pre-determined primary piece of content; and 
 generating a list of scores for a set of customers, the list of scores indicating a likelihood that a set of customers will interact with the pre-determined primary piece of content, a likelihood that the set of customers will not interact with the pre-determined primary piece of content, or both, based at least in part upon the set of scoring affinities and the set of weights. 
   
     
     
         22 . A tangible, non-transitory, machine-readable medium, comprising machine-readable instructions that, when executed by one or more processors, cause the one or more processors to:
 train a machine learning system, by:
 providing data that is used to train the machine learning system includes both data that the machine learning system would receive in actual use and data relating to the actual results of the input data, the data relating to the actual results providing a baseline for predicting results of cases where a result is unknown; 
 though hindcasting, compare predicted results to subsequent actual results to determine which features produce correct results with the highest level of accuracy, which features produce incorrect results, or both; and 
 generate feature weights based upon the hindcasting.