GENERATING AUDIENCE LOOKALIKE MODELS
Systems and methods described herein may determine audiences for content delivery. A server receives configuration inputs for identifying a target audience from a content-generating user. The configuration inputs indicate the target audience, baseline audience, and special audience. The target audience includes targeted end-users for the content. The baseline audience includes a broad population of end-users having a population characteristic (e.g., geography). The special audience includes a selected population of end-users having a special characteristic in the topics previously accessed by the end-users, such that the server correlates the baseline audience with the special audience to identify the target audience of target users who accessed a webpage having the topic and who are located in, e.g., a geography. The server uses topics terms from all target users to generate a ranked-list of context terms the content-user applies to predict whether webpages have the target audience.
1 . A computer-implemented method for determining audiences of contextually relevant content distribution, comprising:
receiving, by a computer, one or more configuration inputs via a user interface of a content-user, the one or more configuration inputs indicating a target audience and one or more context terms;
identifying, by the computer, a set of target users associated with the one or more context terms defining the target audience, by cross-referencing a first plurality of end-users of a special audience against a second plurality of end-users of a background audience;
identifying, by the computer, a ranked-order list of context terms associated with each target end-user of the target audience; and
training, by the computer, a classifier to predict a probability of a lookalike audience for a webpage by applying the classifier on the ranked-order list of context terms associated with the target audience.
2 . The method according to claim 1 , further comprising applying, by the computer, the classifier on a plurality of topic terms of the webpage to predict a likelihood of the lookalike audience for the webpage.
3 . The method according to claim 1 , further comprising generating, by the computer, the special audience based upon user data of each end-user in the first plurality of end-users of the special audience according to the one or more configuration inputs.
4 . The method according to claim 3 , further comprising updating, by the computer, the special audience according to additional user data received from one or more client devices.
5 . The method according to claim 1 , further comprising selecting, by the computer, the background audience from a database according to a background feature indicated by the one or more configuration inputs.
6 . The method according to claim 5 , wherein selecting the background audience includes, extracting, by the computer, a sample subset of user data records from the database for the second plurality of end-users of the background audience.
7 . The method according to claim 1 , further comprising determining, by the computer, a plurality of co-occurrence probabilities for a plurality of topic terms in a plurality of corpus webpages.
8 . The method according to claim 1 , further comprising:
for a particular end-user, identifying, by the computer, one or more historic webpages accessed by the particular end-user;
identifying, by the computer, a plurality of topic terms of the one or more historic webpages accessed by the particular end-user; and
updated, by the computer, a data record for the particular end-user to include the plurality of topic terms.
9 . The method according to claim 1 , further comprising transmitting, by the computer to a client device, instructions for displaying the target audience via the user interface of the client device.
10 . The method according to claim 1 , further comprising:
receiving, by the computer from a bid server, an availability list of a plurality of available webpages requesting bids; and
for each available webpage of a bid stream, generating, by the computer, the probability of the lookalike audience for the available webpage by applying the classifier on a plurality of topic terms of the available webpage.
11 . A system for determining audiences of contextually relevant content distribution, comprising:
a computer having at least one processor, configured to:
receive one or more configuration inputs via a user interface of a content-user, the one or more configuration inputs indicating a target audience and one or more context terms;
identify a set of target users associated with the one or more context terms defining the target audience, by cross-referencing a first plurality of end-users of a special audience against a second plurality of end-users of a background audience;
identify a ranked-order list of context terms associated with each target end-user of the target audience; and
train a classifier to predict a probability of a lookalike audience for a webpage by applying the classifier on the ranked-order list of context terms associated with the target audience.
12 . The system according to claim 11 , wherein the computer is further configured to apply the classifier on a plurality of topic terms of the webpage to predict a likelihood of the lookalike audience for the webpage.
13 . The system according to claim 11 , wherein the computer is further configured to generate the special audience based upon user data of each end-user in the first plurality of end-users of the special audience according to the one or more configuration inputs.
14 . The system according to claim 13 , wherein the computer is further configured to update the special audience according to additional user data received from one or more client devices.
15 . The system according to claim 11 , wherein the computer is further configured to select the background audience from a database according to a background feature indicated by the one or more configuration inputs.
16 . The system according to claim 15 , wherein when selecting the background audience, the computer is further configured to extract a sample subset of user data records from the database for the second plurality of end-users of the background audience.
17 . The system according to claim 11 , wherein the computer is further configured to determine a plurality of co-occurrence probabilities for a plurality of topic terms in a plurality of corpus webpages.
18 . The system according to claim 11 , wherein the computer is further configured to:
for a particular end-user, identify one or more historic webpages accessed by the particular end-user;
identify a plurality of topic terms of the one or more historic webpages accessed by the particular end-user; and
update a data record for the particular end-user to include the plurality of topic terms.
19 . The system according to claim 11 , wherein the computer is further configured to transmit, to a client device, instructions for displaying the target audience via the user interface of the client device.
20 . The system according to claim 11 , wherein the computer is further configured to:
receive, from a bid server, an availability list of a plurality of available webpages requesting bids; and
for each available webpage of a bid stream, generate, by the computer, the probability of the lookalike audience for the available webpage by applying the classifier on a plurality of topic terms of the available webpage.