User Intention modeling for Web navigation

• US 20030212760A1
• Filed: 05/09/2002
• Published: 11/13/2003
• Est. Priority Date: 05/09/2002
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Chen, Zheng, Sun, Xiaoming, Wenyin, Liu
• Granted Patent: US 6,993,586 B2
• Time in Patent Office: Days
• US Class Current: 709/218
• CPC Class Codes: G06F 16/954 Navigation, e.g. using categorised browsing
• CPC Class Codes: G06F 17/18 for evaluating statistical data , e.g. average values, frequency distributions, probability functions, regression analysis forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04
• CPC Class Codes: H04L 67/564 Enhancement of application control based on intercepted application data
• CPC Class Codes: H04L 67/568 Storing data temporarily at an intermediate stage, e.g. caching
• CPC Class Codes: H04L 67/62 Establishing a time schedule for servicing the requests
• CPC Class Codes: H04L 67/63 Routing a service request depending on the request content or context
• CPC Class Codes: H04L 69/329 in the application layer [OSI layer 7]
• CPC Class Codes: H04L 9/40 Network security protocols

Abstract

The disclosed subject matter models or predicts a user'"'"'s intention during network or WWW navigation. Specifically, a statistical multi-step n-gram probability model is used to predict a user'"'"'s optimal information goal. The optimal information goal is based on a sequence of previously visited network content pieces and a globally optimized navigation path through the sequence.

Claims

1. A method for modeling a user intention during network navigation, the method comprising:

predicting, based on a statistical multi-step n-gram probability model, an optimal information goal of the user; and

wherein the optimal information goal is based on a sequence of previously visited network content pieces and a globally optimized navigation path through the sequence.

2. A method as recited in claim 1, wherein a content piece of the sequence comprises a Web page.

3. A method as recited in claim 1, wherein predicting the globally optimized navigation path is dynamically performed responsive to user Web navigation.

4. A method as recited in claim 1, wherein predicting the optimal information goal further comprises:

recording a history of user action, the history comprising information corresponding to user navigation to a plurality of networked content pieces, the information further indicating at least the sequence of previously visited network content pieces;

for at least a portion of the sequence data, calculating respective probabilities that a user would visit a particular content piece n in the sequence from a content piece n−

1 in the sequence; and

wherein predicting the optimal information goal is based on the respective probabilities.

5. A method as recited in claim 4, wherein the portion comprises a session of user access to particular ones of the networked content pieces.

6. A method as recited in claim 4, wherein the history further comprises a URL, a hyperlink, a user name, a start time, text corresponding to a URL, a query, elapsed time, or an object name.

7. A method as recited in claim 4, wherein calculating respective probabilities that the user would visit a particular content piece n in the sequence from a content piece n−

1 in the sequence is performed based on the following;

8. A method as recited in claim 7, wherein the globally optimized navigation path is based on the following:

9. A method as recited in claim 7, wherein the globally optimized navigation path is based on the following:

10. A method as recited in claim 9 further comprising dynamically determining how many steps to the optimal information goal are to be predicted by employing a perplexity that reflects the entropy of the globally optimized navigation path.

11. A method as recited in claim 9, wherein the optimal information goal is determined according to the following:

12. A computer-readable medium for modeling a user intention during network navigation, the computer-readable medium comprising computer-executable instructions for:

predicting, based on a statistical multi-step n-gram probability model, an optimal information goal of the user; and

wherein the optimal information goal is based on a sequence of previously visited network content pieces and a globally optimized navigation path through the sequence.

13. A computer-readable medium as recited in claim 12, wherein a content piece of the sequence comprises a Web page.

14. A computer-readable medium as recited in claim 12, wherein predicting the globally optimized navigation path is dynamically performed responsive to user Web navigation.

15. A computer-readable medium as recited in claim 12, wherein the instructions for predicting the optimal information goal further comprise instructions for:

recording a history of user action, the history comprising information corresponding to user navigation to a plurality of networked content pieces, the information further indicating at least the sequence of previously visited network content pieces;

for at least a portion of the sequence data, calculating respective probabilities that a user would visit a particular content piece n in the sequence from a content piece n−

1 in the sequence; and

wherein predicting the optimal information goal is based on the respective probabilities.

16. A computer-readable medium as recited in claim 15, wherein the portion comprises a session of user access to particular ones of the networked content pieces.

17. A computer-readable medium as recited in claim 15, wherein the history further comprises a, URL, a hyperlink, a user name, a start time, text corresponding to a URL, a query, elapsed time, or an object name.

18. A computer-readable medium as recited in claim 15, wherein calculating respective probabilities that the user would visit a particular content piece n in the sequence from a content piece n−

1 in the sequence is performed based on the following;

19. A computer-readable medium as recited in claim 17, wherein the globally optimized navigation path is based on the following:

20. A computer-readable medium as recited in claim 17, wherein the globally optimized navigation path is based on the following:

21. A computer-readable medium as recited in claim 20 further comprising instructions for dynamically determining how many steps to the optimal information goal are to be predicted by employing a perplexity that reflects the entropy of the globally optimized navigation path.

22. A computer-readable medium as recited in claim 20, wherein the optimal information goal is determined according to the following:

23. A computing device for modeling a user intention during network navigation, the computing device comprising:

a processor; and

a memory coupled to the processor, the memory comprising computer-executable instructions that are fetched and executed by the processor for;

predicting, based on a statistical multi-step n-gram probability model, an optimal information goal of the user; and

wherein the optimal information goal is based on a sequence of previously visited network content pieces and a globally optimized navigation path through the sequence.

24. A computing device as recited in claim 23, wherein a content piece of the sequence comprises a Web page.

25. A computing device as recited in claim 23, wherein predicting the globally optimized navigation path is dynamically performed responsive to user Web navigation.

26. A computing device as recited in claim 23, wherein the instructions for predicting the optimal information goal further comprise instructions for:

recording a history of user action, the history comprising information corresponding to user navigation to a plurality of networked content pieces, the information further indicating at least the sequence of previously visited network content pieces;

for at least a portion of the sequence data, calculating respective probabilities that a, user would visit a particular content piece n in the sequence from a content piece n−

1 in the sequence; and

wherein predicting the optimal information goal is based on the respective probabilities.

27. A computing device as recited in claim 26, wherein the portion comprises a session of user access to particular ones of the networked content pieces.

28. A computing device as recited in claim 26, wherein the history further comprises a URL, a hyperlink, a user name, a start time, text corresponding to a URL, a query, elapsed time, or an object name.

29. A computing device as recited in claim 26, wherein calculating respective probabilities that the user would visit a particular content piece n in the sequence from a content piece n−

1 in the sequence is performed based on the following;

30. A computing device as recited in claim 29, wherein the globally optimized navigation path is based on the following:

31. A computing device as recited in claim 29, wherein the globally optimized navigation path is based on the following:

32. A computing device as recited in claim 31 further comprising instructions for dynamically determining how many steps to the optimal information goal are to be predicted by employing a perplexity that reflects the entropy of the globally optimized navigation path.

33. A computing device as recited in claim 31, wherein the optimal information goal is determined according to the following:

34. A computing device for modeling a user intention during network navigation, the computing device comprising processing means for:

predicting, based on a statistical multi-step n-gram probability model, an optimal information goal of the user; and

wherein the optimal information goal is based on a sequence of previously visited network content pieces and a globally optimized navigation path through the sequence.

35. A computing device as recited in claim 34, wherein a content piece of the sequence comprises a Web page.

36. A computing device as recited in claim 34, wherein predicting the globally optimized navigation path is dynamically performed responsive to user Web navigation.

37. A computing device as recited in claim 34, wherein the means for predicting the optimal information goal further comprise means for:

recording a history of user action, the history comprising information corresponding to user navigation to a plurality of networked content pieces, the information further indicating at least the sequence of previously visited network content pieces;

for at least a portion of the sequence data, calculating respective probabilities that a user would visit a particular content piece n in the sequence from a content piece n−

1 in the sequence; and

wherein predicting the optimal information goal is based on the respective probabilities.

38. A computing device as recited in claim 37, wherein the portion comprises a session of user access to particular ones of the networked content pieces.

39. A computing device as recited in claim 37, wherein the history further comprises a URL, a hyperlink, a user name, a start time, text corresponding to a URL, a query, elapsed time, or an object name.

40. A computing device as recited in claim 37, wherein calculating respective probabilities that the user would visit a particular content piece n in the sequence from a content piece n−

1 in the sequence is performed based on the following;

41. A computing device as recited in claim 40, wherein the globally optimized navigation path is based on the following:

42. A computing device as recited in claim 40, wherein the globally optimized navigation path is based on the following:

43. A computing device as recited in claim 42 further comprising means for dynamically determining how many steps to the optimal information goal are to be predicted by employing a perplexity that reflects the entropy of the globally optimized navigation path.

44. A computing device as recited in claim 42, wherein the optimal information goal is determined according to the following: