RANKING COMMERCIAL OFFERS BASED ON USER FINANCIAL DATA

• US 20090198602A1
• Filed: 01/31/2008
• Published: 08/06/2009
• Est. Priority Date: 01/31/2008
• Status: Abandoned Application

• Current Assignee: Intuit, Inc.
• Original Assignee: Intuit, Inc.
• Inventors: Wang, Gang, Smallwood, Caitlin
• Application Number:
  US12/023,959
• Publication Number:
  US 20090198602A1
• Time in Patent Office: Days
• US Class Current: 705/35
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes

Abstract

One embodiment of the present invention provides a system that ranks commercial offers for a user in a set of users. During operation, the system receives financial data for the set of users, wherein the financial data includes spending data for the set of users across a set of categories. Next, for a given user, the system computes an individual-strength vector based on financial data specific to the given user, wherein each entry in the individual-strength vector represents an amount of spending in a respective category for the given user. The system additionally computes a relative-strength vector for the given user based on the financial data for the set of users, wherein the relative-strength vector represents a relative-spending strength across the set of categories for the given user relative to the set of users. The system then ranks the commercial offers for a user based on both the set of individual-strength vectors and the set of relative-strength vectors for the set of users.

Claims

1. A method for ranking commercial offers for a user in a set of users, comprising:

receiving a commercial offer for the set of users;

receiving financial data for the set of users, wherein the financial data includes spending data for the set of users across a set of categories;

for a given user in the set of users;

computing an individual-strength vector based on the financial data specific to the given user, wherein each entry in the individual-strength vector represents an amount of spending in a respective category for the given user; and

computing a relative-strength vector based on the financial data for the set of users, wherein the relative-strength vector represents a relative-spending-strength across the set of categories for the given user relative to the set of users; and

computing a ranking score of the commercial offer for a user in the set of users based on both the set of individual-strength vectors and the set of relative-strength vectors for the set of users, wherein computing the ranking score involves computing a set of differences in spending strength between the user and each of the set of users.

2. The method of claim 1, wherein computing the individual-strength vector for the given user involves:

classifying the spending data for the given user into the set of categories; and

generating a vector entry for each of the set of categories by;

computing a total amount of spending within the category; and

normalizing the total amount of spending within the category to a total amount of spending for the given user across the set of categories.

3. The method of claim 1, wherein prior to computing the relative-strength vector, the method further comprises:

classifying spending data for the set of users into the set of categories so that each category is associated with a set of spending records for the set of users; and

for each category,

creating a set of bins, wherein each bin represents an interval of spending strength for the category;

generating a histogram by classifying and aggregating the set of users into the set of bins based on the set of spending records; and

generating a probability distribution function from the histogram.

4. The method of claim 3, wherein computing the relative-strength vector for the given user involves generating a vector entry value for each category in the set of categories by computing a position of the given user in a corresponding probability distribution function.

5. The method of claim 3, wherein the spending strength can be measured by:

an absolute amount of spending in the category;

or

a percentage of spending in the category relative to the total spending across the set of categories.

6. The method of claim 1, wherein prior to computing the ranking score, the method further comprises forming a spending vector for the given user by combining the individual-strength vector and the relative-strength vector, thereby generating a set of spending vectors for the set of users.

7. The method of claim 6, wherein for each commercial offer, computing the ranking score for the user involves:

generating a set of initial ranking scores for the set of users based on the set of individual-strength vectors associated with the set of users;

computing a set of weights for the set of initial ranking scores by computing differences between the spending vector associated with the user and the spending vectors associated with each of the set of users; and

computing a final ranking score for the user based on the set of initial ranking scores and the set of weights.

8. The method of claim 7, wherein generating an initial ranking score for a user in the set of users involves:

classifying the commercial offer into a corresponding category in the set of categories; and

determining the initial ranking score for the user based on the value of an entry for the corresponding category in the associated individual-strength vector.

9. The method of claim 8, wherein determining the initial ranking score for the user based on the value of the entry involves assigning a larger initial ranking score if the value of the entry indicates a larger amount of spending in the corresponding category relative to other categories for the given user.

10. The method of claim 7, wherein computing a difference between a pair of spending vectors involves computing a distance between the pair of spending vectors, wherein a smaller distance indicates a stronger similarity between the pair of spending vectors.

11. The method of claim 10, wherein the distance between the pair of spending vectors can be measured by:

a Euclidean distance;

a Manhattan distance;

Pearson coefficients; and

other vector-distance measures.

12. The method of claim 10, wherein computing the final ranking score R(u, o) of the commercial offer o for the user u based on the set of initial ranking scores and the set of weights involves aggregating a set of weighted initial ranking scores by using the expression:

R(u, o)=KΣ

<sub>u′

∈

N</sub>sim(u, u′

)×

R(u′

, o)

wherein K is a normalization constant, u′

is a user in the set of users N, R(u′

, o) is the initial ranking score of the commercial offer o for the user u′

, and sim(u, u′

) is the distance between the spending vectors associated with the users u and u′

.

13. The method of claim 1, wherein both the individual-strength vector and the relative-strength vector are time-dependent vectors.

14. The method of claim 1, wherein the financial data for a user in the set of users can include information of:

frequency of transactions;

number of transactions;

time interval between transactions; and

amount per transaction.

15. A computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method for ranking commercial offers for a user in a set of users, the method comprising:

receiving a commercial offer for the set of users;

receiving financial data for the set of users, wherein the financial data includes spending data for the set of users across a set of categories;

for a given user in the set of users;

computing an individual-strength vector based on the financial data specific to the given user, wherein each entry in the individual-strength vector represents an amount of spending in a respective category for the given user; and

computing a relative-strength vector based on the financial data for the set of users, wherein the relative-strength vector represents a relative-spending-strength across the set of categories for the given user relative to the set of users; and

computing a ranking score of the commercial offer for a user in the set of users based on both the set of individual-strength vectors and the set of relative-strength vectors for the set of users, wherein computing the ranking score involves computing a set of differences in spending strength between the user and each of the set of users.

16. The computer-readable storage medium of claim 15, wherein computing the individual-strength vector for the given user involves:

classifying the spending data for the given user into the set of categories; and

generating a vector entry for each of the set of categories by;

computing a total amount of spending within the category; and

normalizing the total amount of spending within the category to a total amount of spending for the given user across the set of categories.

17. The computer-readable storage medium of claim 15, wherein prior to computing the relative-strength vector, the method further comprises:

classifying spending data for the set of users into the set of categories so that each category is associated with a set of spending records for the set of users; and

for each category,

creating a set of bins, wherein each bin represents an interval of spending strength for the category;

generating a histogram by classifying and aggregating the set of users into the set of bins based on the set of spending records; and

generating a probability distribution function from the histogram.

18. The computer-readable storage medium of claim 17, wherein computing the relative-strength vector for the given user involves generating a vector entry value for each category in the set of categories by computing a position of the given user in a corresponding probability distribution function.

19. The computer-readable storage medium of claim 17, wherein the spending strength can be measured by:

an absolute amount of spending in the category;

or

a percentage of spending in the category relative to the total spending across the set of categories.

20. The computer-readable storage medium of claim 15, wherein prior to computing the ranking score, the method further comprises forming a spending vector for the given user by combining the individual-strength vector and the relative-strength vector, thereby generating a set of spending vectors for the set of users.

21. The computer-readable storage medium of claim 20, wherein for each commercial offer, computing a ranking score for the user involves:

generating a set of initial ranking scores for the set of users based on the set of individual-strength vectors associated with the set of users;

computing a set of weights for the set of initial ranking scores by computing differences between the spending vector associated with the user and the spending vectors associated with each of the set of users; and

computing a final ranking score for the user based on the set of initial ranking scores and the set of weights.

22. The computer-readable storage medium of claim 21, wherein generating an initial ranking score for a user in the set of users involves:

classifying the commercial offer into a corresponding category in the set of categories; and

determining the initial ranking score for the user based on the value of an entry for the corresponding category in the associated individual-strength vector.

23. The computer-readable storage medium of claim 22, wherein determining the initial ranking score for the user based on the value of the entry involves assigning a larger initial ranking score if the value of the entry indicates a larger amount of spending in the corresponding category relative to other categories for the given user.

24. The computer-readable storage medium of claim 21, wherein computing a difference between a pair of spending vectors involves computing a distance between the pair of spending vectors, wherein a smaller distance indicates a stronger similarity between the pair of spending vectors.

25. The computer-readable storage medium of claim 24, wherein the distance between the pair of spending vectors can be measured by:

a Euclidean distance;

a Manhattan distance;

Pearson coefficients; and

other vector-distance measures.

26. The computer-readable storage medium of claim 24, wherein computing the final ranking score R(u, o) of the commercial offer o for the user u based on the set of initial ranking scores and the set of weights involves aggregating a set of weighted initial ranking scores by using the expression:

R(u, o)=KΣ

<sub>u′

∈

N</sub>sim(u, u′

)×

R(u′

, o),

wherein K is a normalization constant, u′

is a user in the set of users N, R(u′

, o) is the initial ranking score of the commercial offer o for the user u′

, and sim(u, u′

) is the distance between the spending vectors associated with the users u and u′

.

27. The computer-readable storage medium of claim 15, both the individual-strength vector and the relative-strength vector are time-dependent vectors.

28. The computer-readable storage medium of claim 15, wherein the financial data for a user in the set of users can include information of:

frequency of transactions;

number of transactions;

time interval between transactions; and

amount per transaction.

29. A system that ranks commercial offers for a user in a set of users, comprising:

a receiving mechanism configured to;

receive a commercial offer for the set of users; and

receive financial data for the set of users, wherein the financial data includes spending data for the set of users across a set of categories;

a computing mechanism configured to compute for a given user in the set of users;

an individual-strength vector based on the financial data specific to the given user, wherein each entry in the individual-strength vector represents an amount of spending in a respective category for the given user; and

a relative-strength vector based on the financial data for the set of users, wherein the relative-strength vector represents a relative-spending-strength across the set of categories for the given user relative to the set of users; and

where the computing mechanism is further configured to compute a ranking score of a commercial offer for a user in the set of users based on both the set of individual-strength vectors and the set of relative-strength vectors for the set of users,

wherein computing the ranking score involves computing a set of differences in spending strength between the user and each of the set of users.