MACHINE-LEARNING ARCHITECTURE FOR DEFINING END USER AUDIENCES FOR AUTOMATED ONLINE CONTENT SELECTION

1 . A computer-implemented method for determining users based on databases of webpage corpora for dynamic placement of content on available webpages hosted by third-party webservers, the method comprising:

receiving, by a server, from a client device, a context term with which to identify additional terms;

generating, by the server, a set of context terms including the context term by selecting from a corpus database one or more of a plurality of corpus terms having a relationship with the context term, the corpus database storing text extracted from a plurality of historic webpages;

calculating, by the server, an implication score for a plurality of topic terms based on a co-occurrence between each topic term and one or more of the set of context terms on a set of historic webpages associated with the topic term;

selecting, by the server, a particular topic term from the plurality of topic terms based on the implication score for the particular topic term;

determining, by the server, an audience representing a set of one or more users having accessed at least one of the set of historic webpages associated with the particular topic term; and

storing, by the server into a campaign database, campaign data comprising the audience representing the set of one or more users, the set of context terms, and the particular topic term, the campaign data configured for executing a real-time bidding selection operation for an available webpage being accessed by a user of the audience hosted by one or more third-party servers during the real-time bidding selection operation.

2 . The method according to claim 1 , further comprising determining, by the server, the plurality of topic terms from a plurality of terms on the plurality of historic webpages accessed by a plurality of users.

3 . The method according to claim 1 , further comprising maintaining, by the server, an association between each user of a plurality of users based on the user having accessed at least one of the plurality of historic webpages associated with the topic term; and

wherein determining the audience further comprises identifying the set of one or more users of the audience using the association between each user of the plurality of users with the particular topic term.

4 . The method according to claim 1 , further comprising ranking, by the server, a plurality of users for the topic term based on a number of times that each user of the plurality of users accessed at least one of the plurality of historic webpages associated with the topic term.

5 . The method according to claim 1 , further comprising:

identifying, by the server, from a request for a selection value, an identifier for the user from the set of one or more users of the audience; and

transmitting, by the server, to a content exchange server, the selection value of a content provider in response to identifying the identifier for the user.

6 . The method according to claim 1 , further comprising receiving, by the server, from the client device, an audience size defining a number of users to be selected from a plurality of the users for the audience; and

wherein determining the audience includes identifying the set of one or more users of the audience from a plurality of users based on the audience size.

7 . The method according to claim 1 , further comprising generating, by the server, a plurality of phrases for the topic term using a plurality of terms on the plurality of historic webpages from which the topic term is determined.

8 . The method according to claim 1 , wherein calculating the implication score further comprises calculating the implication score based on a number of occurrences of at least one of the set of context terms on at least one of the plurality of historic webpages associated with the topic term.

9 . The method according to claim 1 , wherein generating the set of context terms further comprises (i) selecting a first subset of corpus terms from the plurality of corpus terms based on the context term and (ii) removing a second subset of corpus terms from the first subset of corpus terms using an out-of-context term received from the client device.

10 . The method according to claim 1 , further comprising transmitting, by the server, the plurality of topic terms for display on a graphical user interface (GUI) of the client device.

11 . A system for determining users based on databases of webpage corpora for dynamic placement of content on available webpages hosted by third-party webservers, the system comprising:

non-transitory media containing one or more databases including a corpus database configured to store a plurality of historic webpages and a campaign database configured to store campaign data; and

a server having at least one processor coupled with memory, configured to:

receive, from a client device, a context term with which to identify additional terms;

generate a set of context terms including the context term by selecting from text of the corpus database one or more of a plurality of corpus terms having a relationship with the context term;

calculate an implication score for a plurality of topic terms based on a co-occurrence between each topic term and one or more of the set of context terms on a set of historic webpages associated with the topic term;

select a particular topic term from the plurality of topic terms based on the implication score for the particular topic term;

determine an audience representing a set of one or more users having accessed at least one of the set of historic webpages associated with the particular topic term; and

store the campaign data into the campaign database, the campaign data configured for executing a real-time bidding selection operation for an available webpage being accessed by a user of the audience hosted by one or more third-party servers during the real-time bidding selection operation.

12 . The system according to claim 11 , wherein the server is further configured to determine the plurality of topic terms from a plurality of terms on the plurality of historic webpages accessed by a plurality of users.

13 . The system according to claim 11 , wherein the server is further configured to:

maintain an association between each user of a plurality of users based on the user having accessed at least one of the plurality of historic webpages associated with the topic term, and

determine the audience using the association between each user of the plurality of users with the particular topic term.

14 . The system according to claim 11 , wherein the server is further configured to rank a plurality of users for the topic term based on a number of times that each user of the plurality of users accessed at least one of the plurality of historic webpages associated with the topic term.

15 . The system according to claim 11 , wherein the server is further configured to:

identify, from a request for a selection value, an identifier for the user from the set of one or more users of the audience; and

transmit, to a content exchange server, the selection value of a content provider in response to identifying the identifier for the user.

16 . The system according to claim 11 , wherein the server is further configured to receive, from the client device, an audience size defining a number of users to be selected from a plurality of the users for the audience; and

wherein, when determining the audience, the server is further configured to identify the set of one or more users of the audience from a plurality of users based on the audience size.

17 . The system according to claim 11 , wherein the server is further configured to transmit the plurality of topic terms for display on a graphical user interface (GUI) of the client device.

18 . A non-transitory computer readable medium containing machine-executable program instructions, wherein execution of the program instructions by one or more processors of a computer system causes the one or more processors to execute the steps of:

receiving, from a client device, a context term with which to identify additional terms;

generating a set of context terms including the context term by selecting from a corpus database one or more of a plurality of corpus terms having a relationship with the context term, the corpus database storing text extracted from a plurality of historic webpages;

calculating an implication score for a plurality of topic terms based on a co-occurrence between each topic term and one or more of the set of context terms on a set of historic webpages associated with the topic term;

selecting a particular topic term from the plurality of topic terms based on the implication score for the particular topic term;

determining an audience representing a set of one or more users having accessed at least one of the set of historic webpages associated with the particular topic term; and

storing, into a campaign database, campaign data comprising the audience representing the set of one or more users, the set of context terms, and the particular topic term, the campaign data configured for executing a real-time bidding selection operation for an available webpage being accessed by a user of the audience hosted by one or more third-party servers during the real-time bidding selection operation.

19 . The non-transitory computer readable medium according to claim 18 , wherein the execution of the program instructions by the one or more processors of the computer system further causes the one or more processors to execute the steps of:

maintaining an association between each user of a plurality of users based on the user having accessed at least one of the plurality of historic webpages associated with the topic term, and

determining the audience using the association between each user of the plurality of users with the particular topic term.

20 . The non-transitory computer readable medium according to claim 18 , wherein the execution of the program instructions by the one or more processors of the computer system further causes the one or more processors to execute the steps of:

identifying, from a request for a selection value, an identifier for the user from the set of one or more users of the audience; and

transmitting, to a content exchange server, the selection value of a content provider in response to identifying the identifier for the user.