Behavioral targeting system

1. A method for behavioral targeting comprising:

receiving, by a processor, a plurality of events generated by a user;

classifying, by the processor, each of the plurality of received events in one of a plurality of categories;

selecting, by the processor as each of the plurality of events is received, a model from a plurality of models based on a marketing objective, the selected model comprising rules used for dimension processing;

applying, by the processor upon the selection, the rules of the selected model to user data comprising at least the plurality of received events for determining interest of the user in the plurality of categories into which the plurality of received events are classified;

generating, by the processor, a plurality of short-term raw user interest scores of the user for the plurality of categories into which the plurality of received events are classified via the application of the rules, wherein the short-term raw user interest scores are indicative of short-term interest of the user in the plurality of categories into which the plurality of received events are classified; and

communicating, by the processor to an advertising server, the short-term raw user interest scores for enabling serving advertisements for small purchases customized to the short term interests of the user, the small purchases comprising purchases made in a pre-defined period of time.

2. The method of claim 1 , further comprising:

converting, by the processor, the raw user interest scores to at least a mapped score indicative of a tendency of the user for a particular action.

3. The method of claim 1 , wherein the dimension processing comprises recency, intensity and frequency processing.

4. The method of claim 3 , wherein each of the plurality of models is associated with a respective category and each model comprises respective weights for the recency, the intensity and the frequency processing based on the respective category and an event type.

5. The method of claim 1 , further comprising:

storing, by the processor, the plurality of user events to a webserver at predetermined time intervals.

6. The method of claim 5 , further comprising:

retrieving, by the processor, the plurality of stored user events in batch processing from an event log of the webserver wherein the predetermined time intervals span a pre-defined long time period;

generating, by the processor, a plurality of long-term raw user interest scores based on the batch processing;

enabling, by the processor, future serving of advertisements by the advertising server via providing the long-term raw user interest scores of the user to the advertising server.

7. The method of claim 6 , further comprising:

converting, by the processor, the plurality of short-term raw user interest scores and the plurality of long term raw user interest scores to respective mapped short-term and long-term user interest scores; and

generating, by the processor, a combined score of the user for the respective category by combining the plurality of mapped short-term user interest scores and the plurality of mapped long-term user interest scores.

8. A computing system for behavioral targeting comprising:

at least one hardware processor;

a storage medium for tangibly storing thereon program logic for execution by the processor, the program logic comprising:

event receiving logic, executed by the processor, for receiving a plurality of events generated by a user;

event classifying logic, executed by the processor, for classifying each of the plurality of received events in one of a plurality of categories;

model selecting logic, executed by the processor, for selecting, as each of the plurality of events is received, a model from a plurality of models based on a marketing objective, the selected model comprising rules used for dimension processing;

model applying logic, executed by the processor, for applying upon execution of the model selection logic, the rules of the selected model to user data comprising at least the plurality of received events for determining interest of the user in the plurality of categories;

short-term raw score generating logic, executed by the processor, for generating at least a plurality of short-term raw user interest scores of the user for the plurality of categories into which the plurality of received events are classified via the application of the rules, the raw user interest scores are indicative of interest of the user in the plurality of categories into which the plurality of received events are classified; and

communicating logic, executed by the processor, for communicating the short-term raw user interest scores to an advertising server for enabling serving advertisements for small purchases customized to the short-term interests of the user, the small purchases comprising purchases made in a pre-defined period of time.

9. The system of claim 8 , further comprising:

converting logic, executed by the processor, for converting the short-term raw user interest scores to respective mapped scores indicative of a tendency of the user for a particular action.

10. The system of claim 8 , further comprising:

storing logic, executed by the processor, for storing the plurality of user events to a web server at predetermined time intervals.

11. The system of claim 10 , the score generating logic further comprises:

long-term raw score generating logic, executed by the processor, for generating a plurality of long-term raw user interest scores for the plurality of categories into which the plurality of received events are classified, each of the plurality of long-term raw user interest scores corresponding to each of the predetermined time intervals at which the plurality of user events are retrieved.

12. The system of claim 11 , further comprising:

future ad-serving logic, executed by the processor, for enabling future serving of an advertisement to the user based on the plurality of long-term raw user interest scores.

13. A non-transitory computer readable storage medium for behavioral targeting, comprising instructions, which when executed by a processor cause the processor to:

receive a plurality of events generated by a user;

classify each of the plurality of received events into one of a plurality of categories;

select, as each of the plurality of events is received, a model from a plurality of models based on a marketing objective, the selected model comprising rules used for dimension processing;

apply, upon the selection, the rules of the selected model to user data comprising at least the plurality of received events for determining interest of the user in the plurality of categories into which the plurality of received events are classified;

generate at least a plurality of short-term raw user interest scores of the user for the plurality of categories into which the plurality of received events are classified via the application of the rules wherein the short-term raw user interest scores are indicative of interest of the user in the plurality of categories into which the plurality of received events are classified; and

communicate the short-term raw user interest scores to an advertising server for enabling serving advertisements for small purchases customized to the short term interests of the user, the small purchases comprising purchases made in a pre-defined period of time.

14. The non-transitory computer readable storage medium of claim 13 , further comprising instructions for the processor to:

store the plurality of user events to a webserver at predetermined time intervals.

15. The non-transitory computer readable storage medium of claim 14 , further comprising instructions for the processor to:

generate a plurality of long-term raw user interest scores for the plurality of categories, each of the plurality of long-term raw user interest scores corresponding to each of the predetermined time intervals at which the plurality of user events are retrieved.