Behavioral Targeting For Tracking, Aggregating, And Predicting Online Behavior
A pre-computed concept map represents concepts, concept metadata, and relationships between the plurality of concepts. Online user behavior may be predicted by correlating one or more online events of a user with one or more features of the concept map, aggregating a concept map history of the user to obtain online behavior over time, aggregating online behavior of the user and one or more other users to obtain aggregated online user behavior, and predicting future online behavior of the user based at least in part on the online behavior of the user and the aggregated online user behavior. The predicted behavior may be used to target ads that the user is likely to find relevant.
1 . A computer implemented method comprising:
correlating one or more online events of a user with one or more features of a pre-computed concept map representing a plurality of concepts, concept metadata, and relationships between the plurality of concepts;
aggregating a concept map history of the user to obtain online behavior over time;
aggregating online behavior of the user and one or more other users to obtain aggregated online user behavior; and
predicting future online behavior of the user based at least in part on the online behavior of the user and the aggregated online user behavior.
2 . The method of claim 1 , further comprising targeting one or more ads to the user based at least in part on the predicted future online behavior of the user.
3 . The method of claim 2 wherein the one or more ads are based at least in part on an amount of time that has elapsed since one or more online events.
4 . The method of claim 1 wherein the one or more online events comprise one or more of:
searching for a keyword;
browsing a webpage;
reading an email; and
shopping.
5 . The method of claim 1 wherein the relationships comprise one or more of:
page co occurrence of concepts;
click through rates (CTRs) of advertisement;
co occurrence of concepts in advertiser campaigns; and
co occurrence of concepts in advertisement creatives.
6 . The method of claim 1 wherein the one or more features comprise one or more of:
a keyword;
a category; and
geographical information.
7 . The method of claim 1 wherein the aggregating a concept map history of the user further comprises storing the online behavior over time information in a profile for the user, the profile comprising one or more of:
high level categories;
aggregate category path for all seed concept nodes;
top communities; and
aggregate list of concept nodes.
8 . The method of claim 1 wherein the aggregating online behavior of the user and one or more other users further comprises increasing a score of a behavioral edge between a first concept and a second concept in the concept map if:
an edge exists between the first concept and the second concept in the concept map;
the first concept and the second concept are seed nodes of two different concept map events of the same user; and
a timestamp of a concept map event of the first concept is after a concept map event of the second concept.
9 . The method of claim 1 wherein the aggregating online behavior of the user and one or more other users further comprises increasing a score of a behavioral edge between a first concept and a second concept in the concept map if:
the first concept and the second concept belong to the same community;
the first concept and the second concept are seed nodes of two different concept map events of the same user; and
a timestamp of a concept map event of the first concept is after a concept map event of the second concept.
10 . The method of claim 1 , further comprising limiting the concept map history that is aggregated based at least in part on one or more of:
a category;
a source;
a time frame;
a community; and
a concept.
11 . An apparatus comprising:
a pre-computed concept map representing concepts, concept metadata, and relationships between the concepts; and
a behavioral targeting engine configured to:
correlate one or more online events of a user with one or more features of the pre-computed concept map;
aggregate a concept map history of the user to obtain online behavior over time;
aggregate online behavior of the user and one or more other users to obtain aggregated online user behavior; and
predict future online behavior of the user based at least in part on the online behavior of the user and the aggregated online user behavior.
12 . The apparatus of claim 11 , further comprising an ad matching engine configured to target one or more ads to the user based at least in part on the predicted future online behavior of the user.
13 . The apparatus of claim 12 wherein the one or more ads are based at least in part on an amount of time that has elapsed since one or more online events.
14 . The apparatus of claim 11 wherein the one or more online events comprise one or more of:
searching for a keyword;
browsing a webpage;
reading an email; and
shopping.
15 . The apparatus of claim 11 wherein the relationships comprise one or more of:
page co occurrence of concepts;
click through rates (CTRs) of advertisement;
co occurrence of concepts in advertiser campaigns; and
co occurrence of concepts in advertisement creatives.
16 . The apparatus of claim 11 wherein the one or more features comprise one or more of:
a keyword;
a category; and
geographical information.
17 . The apparatus of claim 11 wherein the behavioral targeting engine is further configured store the online behavior over time information in a profile for the user, the profile comprising one or more of:
high level categories;
aggregate category path for all seed concept nodes;
top communities; and
aggregate list of concept nodes.
18 . The apparatus of claim 11 wherein the behavioral targeting engine is further configured to increase a score of a behavioral edge between a first concept and a second concept in the concept map if:
an edge exists between the first concept and the second concept in the concept map;
the first concept and the second concept are seed nodes of two different concept map events of the same user; and
a timestamp of a concept map event of the first concept is after a concept map event of the second concept.
19 . The apparatus of claim 11 wherein the behavioral targeting engine is further configured to increase a score of a behavioral edge between a first concept and a second concept in the concept map if:
the first concept and the second concept belong to the same community;
the first concept and the second concept are seed nodes of two different concept map events of the same user; and
a timestamp of a concept map event of the first concept is after a concept map event of the second concept.
20 . The apparatus of claim 11 wherein the behavioral targeting engine is further configured to limit the concept map history that is aggregated based at least in part on one or more of:
a category;
a source;
a time frame;
a community; and
a concept.
21 . An apparatus comprising:
means for correlating one or more online events of a user with one or more features of a pre-computed concept map representing a plurality of concepts, concept metadata, and relationships between the plurality of concepts;
means for aggregating a concept map history of the user to obtain online behavior over time;
means for aggregating online behavior of the user and one or more other users to obtain aggregated online user behavior; and
means for predicting future online behavior of the user based at least in part on the online behavior of the user and the aggregated online user behavior.
24 . A program storage device readable by a machine, embodying a program of instructions executable by the machine to perform a method, the method comprising:
correlating one or more online events of a user with one or more features of a pre-computed concept map representing a plurality of concepts, concept metadata, and relationships between the plurality of concepts;
aggregating a concept map history of the user to obtain online behavior over time;
aggregating online behavior of the user and one or more other users to obtain aggregated online user behavior; and
predicting future online behavior of the user based at least in part on the online behavior of the user and the aggregated online user behavior.