System and method for predicting card member spending using collaborative filtering

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.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

The features of the present invention will become more apparent from the detailed description set forth below when taken in conjunction with the drawings. The left-most digit of a reference number identifies the drawing in which the reference number first appears.

FIG. 1 is a block diagram of an exemplary computer network over which the processes of the present disclosure may be performed.

FIG. 2 is a flowchart of an exemplary collaborative filtering process performed over the network of FIG. 1.

FIG. 3 is a diagram of an exemplary collaborative filtering database for use with the process of FIG. 2.

FIG. 4 is a diagram of exemplary ranking results using the collaborative filtering process of FIG. 2.

FIG. 5 is an exemplary method of practicing the present invention.

FIG. 6 is a detailed illustration of an initialization stage that may be incorporated into the exemplary method of FIG. 5.

FIG. 7 is a detailed illustration of a filtering stage that may be incorporated into the exemplary method of FIG. 5.

FIG. 8 is a detailed flow chart of a transaction size filter and distance filter that may be incorporated into the exemplary filtering stage of FIG. 7.

FIG. 9 is a detailed flow chart of a processing stage that may be used within the exemplary embodiment of FIG. 5.

FIG. 10 is a block diagram of an exemplary computer connected to a network with which the exemplary method of FIG. 5 may be implemented.

Claims

1. A method for predicting spending habits of card members, comprising: (i) identifying financial transactions from a plurality of card members that involve a plurality of restaurants over a period of time;(ii) filtering, for a selected card member of the plurality of card members, the plurality of restaurants to generate a set of candidate restaurants for the selected card member;(iii) processing the set of candidate restaurants to generate a list of recommended restaurants for the selected card member; and(iv) reporting the list of recommended restaurants to the selected card member.

2. The method of claim 1, further comprising repeated steps (i) through (iv) for each card member within the plurality of card members.

3. The method of claim 1, 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;computing, 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; andfiltering the set of candidate restaurants according to at least one of: (i) an average transaction size and (ii) geographic location.

4. The method of claim 3, wherein the set of candidate restaurants represents those non-identified restaurants whose strength of association exceeds a specified threshold.

5. The method of claim 4, further comprising: categorizing the plurality of card members based on a number of financial transactions between each card member and each of the plurality of restaurants; andestablishing the specified threshold value for each card member based on the categorization.

6. The method of claim 3, 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;computing an average transaction size for the identified restaurants over the period of time; andeliminating restaurants from the set of candidate restaurants based on at least the average transaction size of the candidate restaurant.

7. The method of claim 6, further comprising 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.

8. The method of claim 3, 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;computing a standard deviation for the obtained travel distances; andeliminating restaurants from the set of candidate restaurants based on at least the computed standard deviation of the obtained travel distances.

9. The method of claim 8, further comprising 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.

10. The method of claim 1, wherein the processing step further comprises: computing, for each restaurant within the set of candidate restaurants, a probability of acceptance of the candidate restaurant by the selected card member; andprioritizing 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.

11. The method of claim 10, wherein the probability of acceptance represents a probability that the selected card member will have future financial transactions involving the candidate restaurant.

12. The method of claim 11, wherein the computing step further comprises: calculating, for each identified restaurant, a probability that the selected card member will have future financial transactions with the identified restaurant;computing, 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; andcomputing, 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.

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.

15. The method of claim 1, wherein the reporting step further comprises reporting a specified number of recommended restaurants to the selected card member.

16. The method of claim 1, wherein the reporting step further comprises reporting a specified number of restaurants that are within a pre-determined distance of the card member billing address.

17. A system for predicting spending habits of card members, comprising: means for identifying financial transactions from a plurality of card members that involve a plurality of restaurants over a period of time;means for filtering, for a selected card member of the plurality of card members, the plurality of restaurants to generate a set of candidate restaurants for the selected card member; andmeans for processing the set of candidate restaurants to generate a list of recommended restaurants for the selected card member.

18. The system of claim 17, wherein the filtering means further comprises: means for identifying restaurants within the plurality of restaurants that have at least one financial transaction with the selected card member over the period of time;means for computing, for each non-identified restaurant within the plurality of restaurants, a strength of association between each identified restaurant and each non-identified restaurant;means for generating the set of candidate restaurants based on at least the strengths of association; andmeans for filtering the set of candidate restaurants according to at least one of: (i) an average transaction size and (ii) geographic location.

19. The system of claim 18, wherein the set of candidate restaurants represents those non-identified restaurants whose strength of association exceeds a specified threshold.

20. The system of claim 19, further comprising: means for categorizing the plurality of card members based on a number of financial transactions between each card member and each of the plurality of restaurants; andmeans for establishing the specified threshold value for each card member based on the categorization.

21. The system of claim 18, wherein the means for filtering the set of candidate restaurants according to the average transaction size further comprises: means for obtaining an average transaction size for each candidate restaurant over the period of time;means for computing an average transaction size for the identified restaurants over the period of time; andmeans for eliminating restaurants from the set of candidate restaurants based on at least the average transaction size of the candidate restaurant.

22. The system of claim 21, further comprising means for 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.

23. The system of claim 18, wherein the means for filtering the set of candidate restaurants according to at least one of geographic location further comprises: means for obtaining, for each identified restaurant, a distance traveled by the selected card member when visiting the identified restaurant;means for computing a standard deviation for the obtained travel distances; andmeans for eliminating restaurants from the set of candidate restaurants based on at least the computed standard deviation of the obtained travel distances.

24. The system of claim 23, further comprising means for 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.

25. The system of claim 17, wherein the processing means further comprises: means for computing, for each restaurant within the set of candidate restaurants, a probability of acceptance of the candidate restaurant by the selected card member; andmeans for 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.

26. The system of claim 25, wherein the probability of acceptance represents a probability that the selected card member will have financial transactions involving the candidate restaurant.

27. The system of claim 25, wherein the computing means further comprises: means for calculating, for each identified restaurant, a probability that the selected card member will have future financial transactions with the identified restaurant;means for computing, 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; andmeans for computing, 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.

28. The system of claim 27, 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.

29. The system of claim 27, 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.

30. The system of claim 17, further comprising means for reporting the list of recommended restaurants to the selected card member.

31. A computer-based system for predicting spending habits of card members, comprising: a processor; anda memory in communication with the processor for storing a plurality of processing instructions for directing the processor to: identify financial transactions from a plurality of card members that involve a plurality of restaurants over a period of time;filter, for a selected card member of the plurality of card members, the plurality of restaurants to generate a set of candidate restaurants for the selected card member; andprocess the set of candidate restaurants to generate a list of recommended restaurants for the selected card member.