METHODS AND SYSTEMS FOR UTILIZING A TIME FACTOR AND/OR ASYMMETRIC USER BEHAVIOR PATTERNS FOR DATA ANALYSIS

• US 20070265870A1
• Filed: 09/29/2006
• Published: 11/15/2007
• Est. Priority Date: 04/19/2006
• Status: Abandoned Application

• Current Assignee: NEC Laboratories America Inc (NEC Corporation)
• Original Assignee: NEC Laboratories America Inc (NEC Corporation)
• Inventors: Tseng, Belle L., Song, Xiaodan
• Application Number:
  US11/537,018
• Publication Number:
  US 20070265870A1
• Time in Patent Office: Days
• US Class Current: 705/7
• CPC Class Codes: G06Q 30/00 Commerce
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data

Abstract

Methods and systems for data mining and analysis that may be used for capturing user/entity behavior, providing influence filtering and/or providing recommendations. One particular use may be for providing, among other things, personalized recommendations. The methods and systems may include generating an influence network. The influence network may include a user'"'"'s adoption behavior of items. The influence network may further include temporal aspects of information flow or diffusion of information through the network. The influence network may also include adoption time(s) of one or more item(s) between users/entities. Further, the influence network may include asymmetric user/entity adoption behavior. Methods and systems of influence filtering are provided that include generating asymmetric relationship(s) between users and providing a filtering module utilizing the asymmetric relationship(s) between user/entity.

Claims

1. A method of capturing user behavior, comprising the step of:

generating an influence network including users'"'"' adoption behavior of items.

2. The method of claim 1, wherein the influence network further includes time.

3. The method of claim 2, wherein the influence network further includes adoption times of items between users.

4. The method of claim 1, further comprising the step of identifying information propagation through the influence network.

5. The method of claim 1, wherein the influence network includes category of items.

6. The method of claim 1, wherein the influence network includes asymmetric users'"'"' adoption behavior.

7. A method of influence filtering (collaborative filtering directed claim), comprising the steps of:

generating asymmetric relationship(s) between users; and

providing a filtering module utilizing the asymmetric relationship(s) between users.

8. The method of claim 7, wherein the influence filtering is for ranking users and/or items.

9. The method of claim 7, wherein the asymmetric relationship is the user adoption of items between users.

10. The method of claim 7, wherein the influence filtering includes category of items.

11. The method of claim 7, wherein the influence filtering further includes time.

12. A method of using behavior patterns drawn from data, comprising the steps of:

generating an influence network including user adoption behavior for items of interest that comprehends time of adoption; and

determining asymmetric influence between users of the network.

13. The method of claim 12, further comprising the step of providing one or more recommendation(s) to at least one user or entity based on the asymmetric influence between users or entities.

14. The method of claim 12, further comprising the step of determining if the behavior patterns are topic sensitive.

15. The method of claim 12, further comprising the step of sorting the data by category.

16. The method of claim 12, further comprising the step of analyzing historical data from a dataset found in one or more databases.

17. The method of claim 12, wherein the influence network is a mutli-node and multi-path information flow network that includes quantified asymmetric user/entity behavior between various nodes of the network.

18. The method of claim 12, further comprising the steps of:

determining if data has been accessed by a user or a recommendation has been requested; and

providing a recommendation to the user based on an item selected and information flow through the network based on the asymmetric influences between users.

19. The method of claim 18, further comprising the steps of:

determining if an item is adopted by a user; and

updating data in the one or more database(s) to record the adoption of an item by the user.

20. A computer system configured for using behavior patterns drawn from data, comprising:

a first modeling module modeling asymmetric influences between users; and

a second modeling module modeling information propagation that considers time in determining asymmetric influence between the users.

21. The system of claim 20, further comprising:

one or more databases with data identifiers including a user ID, an item ID, and a time stamp.

22. The system of claim 21, further comprising a recommendation module.

23. The system of claim 20, wherein the first modeling module includes an early adoption based information flow network.

24. The system of claim 23, wherein the first modeling module includes a topic sensitive early adoption based information flow network.

25. The system of claim 20, wherein the first modeling module includes a topic sensitive early adoption based information flow network.

26. The system of claim 20, wherein the second modeling module uses summation of various propagation steps to model information propagation.

27. The system of claim 20, wherein the second modeling module uses direct summation to model information propagation.

28. The system of claim 20, wherein the second modeling module uses exponential weighted summation to model information propagation.