SYSTEM AND METHOD OF PREDICTING PURCHASE BEHAVIORS FROM SOCIAL MEDIA

• US 20140244361A1
• Filed: 12/11/2013
• Published: 08/28/2014
• Est. Priority Date: 02/25/2013
• Status: Abandoned Application

• Current Assignee: PayPal, Inc. (PayPal Holdings, Inc.)
• Original Assignee: eBay Inc.
• Inventors: Zhang, Yongzheng, Pennacchiotti, Marco
• Application Number:
  US14/103,062
• Publication Number:
  US 20140244361A1
• Time in Patent Office: Days
• US Class Current: 705/7.33
• CPC Class Codes: G06Q 30/0204 Market segmentation

Abstract

In an example embodiment, a first social media profile is retrieved. Express interests in the first social media profile are extracted, and social media categories corresponding to the express interests are identified. Demographic information is also extracted from the first social media profile. Then, the identified social media categories and demographic information are correlated with ecommerce categories of purchases. Using results from the correlating, a machine learning process is configured, the machine learning process accepting a second social media profile as input and returning a prediction of an ecommerce category as output.

Claims

1. An apparatus comprising:

a processor; and

a memory,

the processor configured to;

retrieve a first social media profile;

extract express interests in the first social media profile;

identify social media categories corresponding to the express interests;

extract demographic information from the first social media profile;

correlate the identified social media categories and demographic information with ecommerce categories of purchases; and

use results from the correlating to configure a machine learning process, the machine learning process accepting a second social media profile as input and returning a prediction of an ecommerce category as output.

2. The apparatus of claim 1, wherein the first social media profile is retrieved from a social media service.

3. The apparatus of claim 2, wherein the social media categories are identified using a schema provided by the social media service.

4. The apparatus of claim 3, wherein the correlating includes obtaining a schema of ecommerce categories of purchases from an ecommerce service.

5. The apparatus of claim 1, wherein the demographic information includes gender information.

6. The apparatus of claim 1, wherein the demographic information includes age information.

7. A method comprising:

retrieving a first social media profile;

extracting express interests in the first social media profile;

identifying social media categories corresponding to the express interests;

extracting demographic information from the first social media profile;

correlating the identified social media categories and demographic information with ecommerce categories of purchases; and

using results from the correlating to configure a machine learning process, the machine learning process accepting a second social media profile as input and returning a prediction of an ecommerce category as output.

8. The method of claim 7, further comprising:

using the machine learning process to recommend one or more items for sale to a user corresponding to the second social media profile in the ecommerce category predicted using the second social media profile.

9. The method of claim 8, wherein the machine learning process also accepts social media communications as input.

10. The method of claim 9, wherein the social media communications include posts.

11. The method of claim 9, wherein the social media communications include friends.

12. The method of claim 9, wherein the social media communications include recommendations.

13. The method of claim 9, wherein the social media communications include check-ins.

14. A non-transitory machine-readable storage medium having embodied thereon instructions executable by one or more machines to perform operations comprising:

retrieving a first social media profile;

extracting express interests in the first social media profile;

identifying social media categories corresponding to the express interests;

extracting demographic information from the first social media profile;

correlating the identified social media categories and demographic information with ecommerce categories of purchases; and

using results from the correlating to configure a machine learning process, the machine learning process accepting a second social media profile as input and returning a prediction of an ecommerce category as output.

15. The non-transitory machine-readable storage medium of claim 14, further comprising:

using the machine learning process to recommend one or more items for sale to a user corresponding to the second social media profile in the ecommerce category predicted using the second social media profile.

16. The non-transitory machine-readable storage medium of claim 15, wherein the machine learning process also accepts social media communications as input.

17. The non-transitory machine-readable storage medium of claim 16, wherein the social media communications include posts.

18. The non-transitory machine-readable storage medium of claim 16, wherein the social media communications include friends.

19. The non-transitory machine-readable storage medium of claim 16, wherein the social media communications include recommendations.

20. The non-transitory machine-readable storage medium of claim 16, wherein the social media communications include check-ins.