DATA CLUSTERING AND USER MODELING FOR NEXT-BEST-ACTION DECISIONS

• US 20140344270A1
• Filed: 05/16/2013
• Published: 11/20/2014
• Est. Priority Date: 05/16/2013
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Lambert, Daniel, Herman, Norbert
• Granted Patent: US 9,251,275 B2
• Time in Patent Office: Days
• US Class Current: 707/737
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 16/955 using information identifiers, e.g. uniform resource locators [URL]
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06Q 10/067 Enterprise or organisation modelling
• CPC Class Codes: G06Q 30/0204 Market segmentation
• CPC Class Codes: G06Q 30/0224 based on user history
• CPC Class Codes: G06Q 30/0255 based on user history
• CPC Class Codes: G06Q 50/01 Social networking

Abstract

Embodiments herein provide data clustering and user modeling for next-best-action decisions. Specifically, a modeling tool is configured to: receive indicators within unstructured social data from a plurality of users; analyze the unstructured social data of each of the plurality of users to assign a set of feature vectors to each of the plurality of users, each feature vector corresponding to one or more personality characteristics of each of the plurality of users; and analyze the feature vectors to identify two or more users from the plurality of users sharing a set of similar feature vectors. The modeling tool is further configured to: group the two or more users from the plurality of users sharing the set of similar feature vectors to form a cluster; identify attributes of the cluster; and input the attributes of the cluster into a predictive model to determine an offer corresponding to the cluster.

Claims

1. A method for data clustering and user modeling for next-best-action decisions, the method comprising the computer-implemented steps of:

receiving unstructured social data of a plurality of users, the unstructured social data comprising one or more indicators that indicate at least one of;

sentiment, personality, and emotional state;

analyzing the unstructured social data of each of the plurality of users to assign a set of feature vectors to each of the plurality of users, each of the set of feature vectors corresponding to one or more personality characteristics of each of the plurality of users;

analyzing the set of feature vectors to identify two or more users from the plurality of users sharing a set of similar feature vectors;

grouping the two or more users from the plurality of users sharing the set of similar feature vectors to form a cluster;

identifying attributes of the cluster; and

inputting the attributes of the cluster into a predictive model to determine a commercial offer that corresponds to the cluster.

2. The method according to claim 1, further comprising combining multiple feature vectors from the set of feature vectors to generate a set of higher-level feature vectors.

3. The method according to claim 1, further comprising receiving structured data of the plurality of users.

4. The method according to claim 3, the structured data comprises a history of past transactions.

5. The method according to claim 1, the analyzing the set of feature vectors comprising:

assigning a value to each of the set of feature vectors for each of the plurality of users, the value identifying a frequency and recentness of one or more indicators that indicate one or more personality characteristics corresponding to each of the set of feature vectors;

grouping each of the set of feature vectors based on the assigned value; and

plotting a graphical representation of the set of feature vectors for each of the plurality of users based on the grouping.

6. The method according to claim 5, further comprising determining a difference between graphical representations of the two or more users from the plurality of users.

7. The method according to claim 1, further comprising selecting the commercial offer for the cluster from a predetermined selection of commercial offers.

8. The method according to claim 7, further comprising:

receiving a response corresponding to the commercial offer; and

evaluating the grouping of the two or more users from the plurality of users of the cluster based on the response to the commercial offer.

9. A computer system for data clustering and user modeling for next-best-action decisions, the system comprising:

at least one processing unit;

memory operably associated with the at least one processing unit; and

a modeling tool storable in memory and executable by the at least one processing unit, the modeling tool comprising;

an analyzing component configured to;

receive unstructured social data from a plurality of users, the unstructured social data comprising one or more indicators that indicate at least one of;

sentiment, personality, and emotional state;

analyze the unstructured social data of each of the plurality of users to assign a set of feature vectors to each of the plurality of users, each of the set of feature vectors corresponding to one or more personality characteristics of each of the plurality of users; and

analyze the set of feature vectors to identify two or more users from the plurality of users sharing a set of similar feature vectors;

a clustering component configured to;

group the two or more users from the plurality of users sharing the set of similar feature vectors to form a cluster; and

identify attributes of the cluster; and

an offering component configured to input the attributes of the cluster into a predictive model to determine a commercial offer that corresponds to the cluster.

10. The computer system according to claim 9, the analyzing component further configured to combine multiple feature vectors from the set of feature vectors to generate a set of higher-level feature vectors.

11. The computer system according to claim 9, the analyzing component further configured to receive structured data of the plurality of users, wherein the structured data comprises a history of past transactions.

12. The computer system according to claim 9, the analyzing component further configured to:

assign a value to each of the set of feature vectors for each of the plurality of users, the value identifying a frequency and recentness of one of more indicators that indicate one or more personality characteristics corresponding to each of the set of feature vectors;

group each of the set of feature vectors based on the assigned value;

plot a graphical representation of the set of feature vectors for each of the plurality of users based on the grouping; and

determine a difference between graphical representations of the two or more users from the plurality of users

13. The computer system according to claim 9, the offering component further configured to select the commercial offer for the cluster from a predetermined selection of commercial offers.

14. The computer system according to claim 13, the offering component further configured to:

receive a response corresponding to the commercial offer; and

evaluate the grouping of the two or more users from the plurality of users of the cluster based on the response to the commercial offer.

15. A computer-readable storage medium storing computer instructions, which when executed, enables a computer system to provide data clustering and user modeling for next-best-action decisions, the computer instructions comprising:

receiving unstructured social data of a plurality of users, the unstructured social data comprising one or more indicators that indicate at least one of;

sentiment, personality, and emotional state;

analyzing the unstructured social data of each of the plurality of users to assign a set of feature vectors to each of the plurality of users, each of the set of feature vectors corresponding to one or more personality characteristics of each of the plurality of users;

analyzing the set of feature vectors to identify two or more users from the plurality of users sharing a set of similar feature vectors;

grouping the two or more users from the plurality of users sharing the set of similar feature vectors to form a cluster;

identifying attributes of the cluster; and

inputting the attributes of the cluster into a predictive model to determine a commercial offer that corresponds to the cluster.

16. The computer-readable storage medium according to claim 15, the computer instructions further comprising combining multiple feature vectors from the set of feature vectors to generate a set of higher-level feature vectors.

17. The computer-readable storage medium according to claim 15, further comprising computer instructions for receiving structured data of the plurality of users, wherein the structured data comprises a history of past transactions.

18. The computer-readable storage medium according to claim 15, the computer instructions for analyzing further comprising:

assigning a value to each of the set of feature vectors for each of the plurality of users, the value identifying a frequency and recentness of one of more indicators that indicate one or more personality characteristics corresponding to each of the set of feature vectors;

grouping each of the set of feature vectors based on the assigned value;

plotting a graphical representation of the set of feature vectors for each of the plurality of users based on the grouping; and

determining a difference between graphical representations of the two or more users from the plurality of users

19. The computer-readable storage medium according to claim 15, further comprising computer instructions for selecting the commercial offer for the cluster from a predetermined selection of commercial offers.

20. The computer-readable storage medium according to claim 19, further comprising computer instructions for:

receiving a response corresponding to the commercial offer; and

evaluating the grouping of the two or more users from the plurality of users of the cluster based on the response to the commercial offer.