SYSTEM AND METHOD FOR AUDIENCE TARGETING
A method and system for audience targeting is disclosed. According to one embodiment, a computer-implemented method comprises logging content consumed by a first group of users. The content is categorized into a set of topics. The set of topics are mapped to a plurality of users in a trade zone. Based on profile matching and according to a topic of an advertisement campaign, a second group of users are identified from the plurality of users.
1 . A computer-implemented method comprising:
logging content visited by a first group of users;
categorizing the content into a set of topics;
mapping the set of topics to a plurality of users in a trade zone; and
identifying a second group of users from the plurality of users based on profile matching of the second group of users with the first group of users according to a topic of an advertisement campaign.
2 . The computer-implemented method of claim 1 , wherein the profile matching is accomplished by fitting a classifier between observed events taken by the first group of users and interest profiles of the first group of users.
3 . The computer-implemented method of claim 1 further comprising learning responses of the first group of users.
4 . The computer-implemented method of claim 3 , wherein the responses of the first group of users comprise clicking, visiting a desired page, downloading a coupon, calling, filling out an application form, watching a video, starting or completing a purchase and interacting with an application.
5 . The computer-implemented method of claim 3 further comprising obtaining the responses of the first group of users from a real-time bidding (RTB) system.
6 . The computer-implemented method of claim 1 further comprising recording time, user ID, and URL of the first group of users.
7 . The computer-implemented method of claim 6 further comprising deriving the set of topics of the content based on the time, user ID, and URL of the first group of users.
8 . The computer-implemented method of claim 7 further comprising obtaining an amount of traffic produced by the first group of users, a time of day the first group of users browse the URL, search queries of the first group of users, location and historical advertisement click rates.
9 . The computer-implemented method of claim 1 further comprising aggregating the content into users' interest and behaviors.
10 . The computer-implemented method of claim 1 further comprising clustering the second set of users into a group with a common interest.
11 . The computer-implemented method of claim 1 further comprising providing a list of the second set of users to a bidder.
12 . A non-transitory computer readable medium having stored thereon computer-readable instructions, and a processor coupled to the non-transitory computer readable medium, wherein the processor executes the computer-readable instructions to:
log content consumed by a first group of users;
categorize the content into a set of topics;
map the set of topics to a plurality of users in a trade zone; and
identify a second group of users from the plurality of users based on profile matching of the second group of users with the first group of users according to a topic of an advertisement campaign.
13 . The non-transitory computer readable medium of claim 12 , wherein the profile matching is accomplished by fitting a classifier between observed events taken by the first group of users and interest profiles of the first group of users.
14 . The non-transitory computer readable medium of claim 12 , wherein the processor executes the computer-readable instructions to learn responses of the first group of users.
15 . The non-transitory computer readable medium of claim 14 , wherein the responses of the first group of users comprise clicking, visiting a desired page, downloading a coupon, calling, filling out an application form, watching a video, starting or completing a purchase, and interacting with an application.
15 . The non-transitory computer readable medium of claim 14 , wherein the responses of the first group of users are obtained from an RTB system.
17 . The non-transitory computer readable medium of claim 12 , wherein the processor executes the computer-readable instructions to record time, user ID, and URL of the first group of users.
18 . The non-transitory computer readable medium of claim 17 , wherein the processor executes the computer-readable instructions to derive the set of topics of the content based on the time, user ID, and URL of the first group of users.
19 . The non-transitory computer readable medium of claim 18 , wherein the processor executes the computer-readable instructions to obtain an amount of traffic produced by the first group of users, a time of day the first group of users browse the URL, search queries of the first group of users, location and historical advertisement click rates.
20 . The non-transitory computer readable medium of claim 12 , wherein the processor executes the computer-readable instructions to aggregate the content into users' interest and behaviors.
21 . The non-transitory computer readable medium of claim 12 wherein the processor executes the computer-readable instructions to cluster the second set of users into a group with a common interest.
22 . The non-transitory computer readable medium of claim 12 wherein the processor executes the computer-readable instructions to provide a list of the second set of users to a bidder.