System and method for predicting card member spending using collaborative filtering

• US 7,792,697 B2
• Filed: 08/08/2006
• Issued: 09/07/2010
• Est. Priority Date: 12/28/2004
• Status: Expired due to Fees

• Current Assignee: Liberty Peak Ventures, LLC (Dominion Harbor Enterprises, LLC)
• Original Assignee: American Express Travel Related Services Company, Inc. (American Express Company)
• Inventors: Baqri, Ali H., Marshall, Vernon, Bhagchandani, Puneet B., Karmali, Siddhesh, Garrett, Rachel B.
• Primary Examiner(s): Bekerman, Michael
• Assistant Examiner(s): Sorkowitz, Daniel M
• Application Number:
  US11/500,492
• Publication Number:
  US 20070143172A1
• Time in Patent Office: 1,491 Days
• Field of Search: 705/14.52, 705/14.53, 705/14.49, 705/14.4
• US Class Current: 705/14.52
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 30/0251 Targeted advertisements
• CPC Class Codes: G06Q 30/0254 based on statistics
• CPC Class Codes: G06Q 30/0255 based on user history
• CPC Class Codes: G06Q 30/0261 based on user location
• CPC Class Codes: G06Q 30/04 Billing or invoicing
• CPC Class Codes: G06Q 50/12 Hotels or restaurants

Abstract

The disclosed method and system allows a credit or charge card issuer to provide its card members with a list of restaurants that might be of interest based on the financial transactions of similar card members. In one instance, this method filters financial transaction data from a plurality of card members that involves a plurality of restaurants to generate a set of candidate restaurant recommendations for a selected card member. This set of candidate restaurant recommendations is processed to yield a list of restaurant recommendations for the selected customer that is prioritized on the basis of the selected card member accepting the recommendation. The list of restaurant recommendations is then reported to the selected card member to enhance card use and marketing.

Claims

1. A computer-based system for predicting spending habits of card members, comprising:

a processor; and

an instruction memory in communication with the processor for storing a plurality of processing instructions for directing the processor to;

produce a data set corresponding to financial transactions of a plurality of card members with a plurality of restaurants;

filter the data set, corresponding to a selected card member of the plurality of card members, to generate a set of candidate restaurants for the selected card member;

generate for the selected card member, a probability of acceptance for each of the candidate restaurants;

wherein the probability of acceptance (P) is determined by;

P=A+(B *(A−

K))

wherein;

A is a number of card-members having transactions with both the candidate restaurant and an identified restaurant;

K is a constant;

B is a value corresponding to a ratio of A to a total number of card member transactions with the identified restaurant; and

a value of (A−

K) is set to zero if its determined value results in a negative number.

2. A computer program product comprising a non transitory computer useable storage medium having computer executable instructions recorded thereon, such instructions comprising:

instructions to produce a data set corresponding to financial transactions of a plurality of card members with a plurality of restaurants;

instructions to filter the data set, corresponding to a selected card member of the plurality of card members, to generate a set of candidate restaurants for the selected card member;

instructions to generate for the selected card member, a probability of acceptance for each of the candidate restaurants;

wherein the probability of acceptance (P) is determined by;

P=A+(B *(A−

K))

wherein;

A is a number of card members having transactions with both the candidate restaurant and an identified restaurant;

K is a constant;

B is a value corresponding to a ratio of A to a total number of card member transactions with the identified restaurant; and

a value of (A−

K) is set to zero if its determined value results in a negative number.

3. A system, comprising:

a processor means for producing a data set corresponding to financial transactions of a plurality of card members with a plurality of restaurants;

a processor means for filtering the data set, corresponding to a selected card member of the plurality of card members, to generate a set of candidate restaurants for the selected card member;

a processor means for generating, for the selected card member, a probability of acceptance for each of the candidate restaurants;

wherein the probability of acceptance (P) is determined by;

P=A+(B *(A−

K))

wherein;

A is a number of card members having transactions with both the candidate restaurant and an identified restaurant;

K is a constant;

B is a value corresponding to a ratio of A to a total number of card member transactions with the identified restaurant; and

a value of (A−

K) is set to zero if its determined value results in a negative number.

4. The system of claim 3, further comprising determining an adventurousness measure of the card member based on the determined probability of acceptance for at least one candidate restaurant.

5. A method comprising:

producing, by a computer, a data set corresponding to financial transactions of a plurality of card members with a plurality of restaurants;

filtering, by the computer, the data set, corresponding to a selected card member of the plurality of card members, to generate a set of candidate restaurants for the selected card member;

generating, by the computer, for the selected card member, a probability of acceptance for each of the candidate restaurants;

wherein the probability of acceptance (P) is determined by;

P=A+(B *(A−

K))

wherein;

A is a number of card members having transactions with both the candidate restaurant and an identified restaurant;

K is a constant;

B is a value corresponding to a ratio of A to a total number of card member transactions with the identified restaurant; and

a value of (A−

K) is set to zero if its determined value results in a negative number.

6. The method of claim 5, wherein the filtering step further comprises:

identifying restaurants within the plurality of restaurants that have at least one financial transaction with the selected card member over the period of time;

determining, for each non-identified restaurant within the plurality of restaurants, a strength of association between each identified restaurant and each non-identified restaurant;

generating the set of candidate restaurants based on at least the strengths of association; and

filtering the set of candidate restaurants according to at least one of;

(i) an average transaction size and (ii) geographic location.

7. The method of claim 6, wherein filtering the set of candidate restaurants according to average transaction size further comprises:

obtaining an average transaction size for each candidate restaurant over the period of time;

determining an average transaction size for the identified restaurants over the period of time; and

eliminating restaurants from the set of candidate restaurants based on at least the average transaction size of the candidate restaurant.

8. The method of claim 7, further comprising before the generating of a probability of acceptance, eliminating restaurants from the set of candidate restaurants whose average transaction size varies from the computed average transaction size by more than a predetermined amount.

9. The method of claim 6, wherein filtering the set of candidate restaurants according to location further comprises:

obtaining, for each identified restaurant, a distance traveled by the selected card member when visiting the identified restaurant;

determining a standard deviation for the obtained travel distances; and

eliminating restaurants from the set of candidate restaurants based on at least the computed standard deviation of the obtained travel distances.

10. The method of claim 9, further comprising, before the generating of a probability of acceptance, eliminating restaurants from the set of candidate restaurants based on the distance between the candidate restaurant and the card member billing address exceeding the computed average distance by a predetermined amount.

11. The method of claim 5, wherein the generating step further comprises

prioritizing each restaurant within the set of candidate restaurants based on the computed probability of acceptance of the candidate restaurant to generate the list of recommended restaurants.

12. The method of claim 5, further comprising:

determining, for each identified restaurant, a probability that the selected card member will have future financial transactions with the identified restaurant;

determining, for each identified restaurant and for each candidate restaurant, a probability that card members within the plurality of card members will have financial transactions at both the identified and the candidate restaurants; and

determining, for each candidate restaurant, the probability that the selected card member will have multiple financial transactions at the candidate restaurant based on;

(i) the probability that the selected card member will have future financial transactions with the identified restaurant and (ii) the probability that the other card members will have financial transactions at both the identified and the candidate restaurant,

wherein the probability of acceptance represents the probability that the selected card member will have multiple future financial transactions involving the candidate restaurant.

13. The method of claim 12, wherein the probability that the selected card member will have at least one future financial transaction with the identified restaurant represents a ratio of a total number of financial transactions involving the selected card member and the identified restaurant to a total number of financial transactions involving the selected card member.

14. The method of claim 12, wherein the probability that card members will have financial transactions at both the identified and the candidate restaurants represents a ratio of a number of financial transactions involving the identified restaurant and the candidate restaurant to the total number of financial transactions involving the identified restaurant.