Method And System For Categorizing Users Browsing Web Content

• US 20140164385A1
• Filed: 12/10/2012
• Published: 06/12/2014
• Est. Priority Date: 12/10/2012
• Status: Abandoned Application

• Current Assignee: ShareThis, Inc.
• Original Assignee: ShareThis, Inc.
• Inventors: LIU, Yandong, QU, Yan
• Application Number:
  US13/709,421
• Publication Number:
  US 20140164385A1
• Time in Patent Office: Days
• US Class Current: 707/740
• CPC Class Codes: G06F 16/337 Profile generation, learning or modification
• CPC Class Codes: G06F 16/35 Clustering; Classification

Abstract

The present invention discloses a method and a web analytics server to categorize a plurality of users browsing one or more web pages. A tracking application module is provided to receive at least one log record, the at least one log record corresponding to one or more user activities from a predefined group of user activities for the plurality of users. Further, a probability generator module is provided to generate a probability data that defines a transition from a current user activity to another user activity in the predefined group of user activities for the plurality of users, and an analytics module is configured to profile the effect of a current user activity to another user activity and to categorize the plurality of users into a plurality of categories based on the probability data.

Claims

1. A computer-implemented method for categorizing a plurality of users browsing one or more web pages, the method comprising:

receiving at least one log record corresponding to one or more user activities from a predefined group of user activities for the plurality of users;

determining a current user activity from the predefined group of user activities for the plurality of users based on the corresponding at least one log record;

generating a probability data that defines a transition from the current user activity to another user activity in the predefined group of user activities for the plurality of users; and

categorizing the plurality of users based on the probability data.

2. The method of claim 1, wherein the at least one log record comprises one or more of cookies representing the plurality of users, timestamps, user activities, sharing channels, and content categories.

3. The method of claim 1, wherein the predefined group of user activities comprises one or more of a viewing activity, a clicking activity, a sharing activity, a searching activity, a visiting activity, an ad exposure activity, an ad clicking activity, and a conversion activity.

4. The method of claim 1 further comprising determining time spent in a transition from a previous user activity to the current user activity.

5. The method of claim 1, wherein the probability data comprises a probability of the transition for a predefined time frame, the predefined time frame corresponding to one or more of a day, a week, a fortnight, and a month.

6. The method of claim 1, wherein the probability data comprises a probability of remaining in the current user activity for a predefined time frame for the plurality of users.

7. The method of claim 1, wherein the probability data comprises:

N-grams for ascertaining transition of the plurality of users from the current user activity to another user activity from the predefined group of user activities; and

one or more probabilities corresponding to the N-grams for the plurality of users.

8. A web analytics server to categorize a plurality of users browsing one or more web pages, the web analytics server comprising:

a tracking application module configured to receive at least one log record, the at least one log record corresponding to one or more user activities from a predefined group of user activities for the plurality of users;

a probability generator module configured to generate a probability data that defines a transition from a current user activity to another user activity in the predefined group of user activities for the plurality of users; and

an analytics module configured to categorize the plurality of users into a plurality of categories based on the probability data.

9. The web analytics server of claim 8 further comprising a tracking component configured to generate at least one log record, the tracking component corresponding to one or more of a widget, a button, a web bug, a web beacon, a hypertext, a tracking pixel, a link on each web page, a local shared object (LSO), and a HyperText Markup Language (HTML) tracking code.

10. The web analytics server of claim 8, wherein the at least one log record comprises at least one of an anonymous cookie, a click log, a sharing log, a timestamp, an event type, a sharing channel, a content identifier, a URL, a domain information and a browser agent information.

11. The web analytics server of claim 8 further comprising a user activity module configured to determine the current user activity for the plurality of users.

12. The web analytics server of claim 8, wherein the predefined group of user activities comprises at least one of a viewing activity, a clicking activity, a sharing activity, a searching activity, a visiting activity, an ad exposure activity, an ad clicking activity, and a conversion activity.

13. The web analytics server of claim 8, wherein the plurality of categories corresponds to various engagement levels in a purchase funnel.

14. The web analytics server of claim 8 further comprising a campaign module configured to deliver one or more web content to the plurality of users based on the plurality of the categories.

15. A computer implemented method for creating a user model comprising a plurality of users browsing one or more web pages, the method comprising:

gathering at least one log record from the one or more web pages;

determining one or more user characteristics based at least in part on the at least one record;

determining probability data that defines a transition of the plurality of users from a current user activity to any other user activity in a predefined group of user activities; and

generating the user model based at least in part on the determined probability and the at least one log record.

16. The computer implemented method of claim 15, wherein the user model is configured to map the plurality of users based on user characteristics and the determined probability data.

17. The computer implemented method of claim 15, wherein the user characteristics corresponds to one or more of the plurality of user activities, content categories, and user preferences.

18. The computer implemented method of claim 15 further comprising delivering a version of the web content from a plurality of versions of the web content to the plurality of users based at least in part on the probability data.

19. The computer implemented method of claim 15, wherein the version of the web content corresponds to a preferred version of web content associated with one of a plurality of categories of the plurality of users.

20. A computer program product for use with a computer, the computer program product embodied on a non-transitory computer readable medium, the computer program product comprising:

programmed instructions to receive at least one log record, the at least one log record corresponding to one or more user activities from a predefined group of user activities for a plurality of users, wherein the predefined group of user activities are retrieved from a data set in a database;

programmed instructions to generate a probability data that defines a transition from a current user activity to another user activity of a web content in the predefined group of user activities for the plurality of users; and

programmed instructions to categorize the plurality of users into a plurality of categories based on the probability data.