Methods and apparatus to generate audience metrics using third-party privacy-protected cloud environments

Abstract

Methods and apparatus to generate audience metrics using third-party privacy-protected cloud environments. An example apparatus includes a data modifier to obtain a first matrix, the first matrix including first data indicative of entities and embeddings, the entities representative of at least one of search result clicks or videos watched, the embeddings representative of at least one of first classifications of the search result clicks or second classifications of the videos watched, generate a second matrix by reducing the first data in the first matrix to second data that satisfies a size corresponding to an input feature, and store the second matrix in first memory as the input feature, and a model generator to generate a demographic correction model based on the second matrix as the input feature, the demographic correction model to correct demographics corresponding to impressions logged in second memory.

1 . A non-transitory computer readable medium comprising instructions that when executed cause at least one processor to:
 obtain a first matrix, the first matrix including first data indicative of entities and embeddings, the entities representative of at least one of search result clicks or videos watched, the embeddings representative of at least one of first classifications of the search result clicks or second classifications of the videos watched;   generate a second matrix by reducing the first data in the first matrix to second data that satisfies a size corresponding to an input feature;   store the second matrix in first memory as the input feature; and   generate a demographic correction model based on the second matrix as the input feature, the demographic correction model to correct demographics corresponding to impressions logged in second memory.   
     
     
         2 . The non-transitory computer readable medium of  claim 1 , wherein the at least one processor is to generate the second matrix based on performing a reduction technique, the at least one processor to perform the reduction technique by:
 selecting a first entity from the entities and first and second embeddings from the embeddings; and   generating the second matrix to include a first entry and a second entry, the first entry storing a first value of the first embedding associated with the first entity, the second entry storing a second value of the second embedding associated with the first entity.   
     
     
         3 . The non-transitory computer readable medium of  claim 1 , wherein the at least one processor is to generate the second matrix based on performing a reduction technique, the at least one processor to perform the reduction technique by:
 calculating weighted averages of the embeddings associated with the entities, the weighted averages including a first weighted average based on a first entity from the entities and ones of the embeddings; and   generating the second matrix to include an entry storing a value of the first weighted average associated with the first entity.   
     
     
         4 . The non-transitory computer readable medium of  claim 1 , wherein the at least one processor is to generate the second matrix based on performing a reduction technique, the at least one processor to perform the reduction technique by:
 calculating values of an average, a Manhattan distance, a Chebyshev distance, a Euclidean distance, or a Minkowski distance of the embeddings associated with the entities, the values including a first value based on a first entity from the entities and ones of the embeddings; and   generating the second matrix to include an entry storing the first value associated with the first entity.   
     
     
         5 . The non-transitory computer readable medium of  claim 1 , wherein the at least one processor is to generate the second matrix based on performing a reduction technique, the at least one processor to perform the reduction technique by:
 selecting at least one of maximum values or minimum values of the embeddings associated with the entities, the at least one of the maximum values or the minimum values including a first value based on a first entity from the entities and ones of the embeddings; and   generating the second matrix to include an entry storing the first value associated with the first entity.   
     
     
         6 . The non-transitory computer readable medium of  claim 1 , wherein the data corresponds to a user access to media, the media associated with the entities and the embeddings. 
     
     
         7 . The non-transitory computer readable medium of  claim 1 , wherein the entities include at least one of top search result click entities or video watch entities. 
     
     
         8 . The non-transitory computer readable medium of  claim 1 , wherein the embeddings include classifications of at least one of Internet searches requested by a user or media accessed by the user. 
     
     
         9 . The non-transitory computer readable medium of  claim 1 , wherein the entities are represented using integer identifiers that map to a knowledge graph. 
     
     
         10 . The non-transitory computer readable medium of  claim 1 , wherein the embeddings are represented as a numerical representation of a class of at least one of objects, images, or words. 
     
     
         11 . An apparatus comprising:
 a data modifier to:
 obtain a first matrix, the first matrix including first data indicative of entities and embeddings, the entities representative of at least one of search result clicks or videos watched, the embeddings representative of at least one of first classifications of the search result clicks or second classifications of the videos watched; 
 generate a second matrix by reducing the first data in the first matrix to second data that satisfies a size corresponding to an input feature; and 
 store the second matrix in first memory as the input feature; and 
   a model generator to generate a demographic correction model based on the second matrix as the input feature, the demographic correction model to correct demographics corresponding to impressions logged in second memory.   
     
     
         12 . The apparatus of  claim 11 , wherein the data modifier is to generate the second matrix based on performing a reduction technique, the data modifier to perform the reduction technique by:
 selecting a first entity from the entities and first and second embeddings from the embeddings; and   generating the second matrix to include a first entry and a second entry, the first entry storing a first value of the first embedding associated with the first entity, the second entry storing a second value of the second embedding associated with the first entity.   
     
     
         13 . The apparatus of  claim 11 , wherein the data modifier is to generate the second matrix based on performing a reduction technique, the data modifier to perform the reduction technique by:
 calculating weighted averages of the embeddings associated with the entities, the weighted averages including a first weighted average based on a first entity from the entities and ones of the embeddings; and   generating the second matrix to include an entry storing a value of the first weighted average associated with the first entity.   
     
     
         14 . The apparatus of  claim 11 , wherein the data modifier is to generate the second matrix is based on performing a reduction technique, the data modifier to perform the reduction technique by:
 calculating values of an average, a Manhattan distance, a Chebyshev distance, a Euclidean distance, or a Minkowski distance of the embeddings associated with the entities, the values including a first value based on a first entity from the entities and ones of the embeddings; and   generating the second matrix to include an entry storing the first value associated with the first entity.   
     
     
         15 . The apparatus of  claim 11 , wherein the data modifier is to generate the second matrix based on performing a reduction technique, the data modifier to perform the reduction technique by:
 selecting at least one of maximum values or minimum values of the embeddings associated with the entities, the at least one of the maximum values or the minimum values including a first value based on a first entity from the entities and ones of the embeddings; and   generating the second matrix to include an entry storing the first value associated with the first entity.   
     
     
         16 . The apparatus of  claim 11 , wherein the data corresponds to a user access to media, the media associated with the entities and the embeddings. 
     
     
         17 . The apparatus of  claim 11 , wherein the entities include at least one of top search result click entities or video watch entities. 
     
     
         18 . The apparatus of  claim 11 , wherein the embeddings include classifications of at least one of Internet searches requested by a user or media accessed by the user. 
     
     
         19 . The apparatus of  claim 11 , wherein the entities are represented using integer identifiers that map to a knowledge graph. 
     
     
         20 . The apparatus of  claim 11 , wherein the embeddings are represented as a numerical representation of a class of at least one of objects, images, or words. 
     
     
         21 - 40 . (canceled)