User intention modeling for web navigation

• US 20060010229A1
• Filed: 09/06/2005
• Published: 01/12/2006
• Est. Priority Date: 05/09/2002
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Chen, Zheng, Sun, Xiaoming, Wenyin, Liu
• Application Number:
  US11/219,932
• Publication Number:
  US 20060010229A1
• Time in Patent Office: Days
• US Class Current: 709/219
• 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 computer-implemented 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. The method of claim 1, wherein a content piece of the sequence comprises a Web page.

3. The method of claim 1, wherein predicting the globally optimized navigation path is dynamically performed responsive to user Web navigation.

4. The method of 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. The method of 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.

6. The method of 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;

7. The method of claim 7, wherein the globally optimized navigation path is based on the following:

8. The method of claim 7, wherein the globally optimized navigation path is based on the following:

9. The method of 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.

10. The method of claim 9, wherein the optimal information goal is determined according to the following:

11. A computer-readable medium for modeling a user intention during network navigation, the computer-readable medium comprising computer-program instructions executable by a 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.

12. The computer-readable medium of claim 12, wherein predicting the globally optimized navigation path is dynamically performed responsive to user Web navigation.

13. The computer-readable medium of 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.

19. 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-program 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.

20. The computing device of 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.

14. The computer-readable medium of 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;

15. The computer-readable medium of claim 17, wherein the globally optimized navigation path is based on the following:

16. The computer-readable medium of claim 17, wherein the globally optimized navigation path is based on the following:

17. The computer-readable medium of 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.

18. The computer-readable medium of claim 20, wherein the optimal information goal is determined according to the following: