Systems and methods for generating location-based group recommendations

• US 10,049,400 B2
• Filed: 11/14/2017
• Issued: 08/14/2018
• Est. Priority Date: 12/07/2011
• Status: Active Grant

• Current Assignee: PayPal, Inc. (PayPal Holdings, Inc.)
• Original Assignee: PayPal, Inc. (PayPal Holdings, Inc.)
• Inventors: Caralis, Jim, Kogan, Nataly, Nakamura, Masumi, Mastroianni, Michael, Sundram, Jason
• Primary Examiner(s): Patel, Mahendra
• Application Number:
  US15/812,829
• Publication Number:
  US 20180068373A1
• Time in Patent Office: 273 Days
• Field of Search: 455456, 455 1, 706 62, 701533
• CPC Class Codes: G06Q 30/0631 Item recommendations
• CPC Class Codes: G06Q 50/01 Social networking
• CPC Class Codes: H04L 67/306 User profiles
• CPC Class Codes: H04W 4/021 Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
• CPC Class Codes: H04W 4/21 for social networking applications

Abstract

Methods and systems for generating location-aware group recommendations are discussed. For example, a method can include operations for receiving a group recommendation request, accessing user profile data associated with members of the group, and generating a group recommendation. The group recommendation request can be received at a network-based system and include identification of a first and second user as well as information identifying a current location associated with the first and second users. Accessing the user profile information can include accessing user profile information for both the first and second users. The group recommendation can be generated based on the current location data and a combination of at least a portion of the user profile data from the first and second users.

Claims

1. A system comprising:

a non-transitory memory storing instructions; and

a processor configured to execute instructions to cause the system to;

receive, over a first network connection with a device, a group recommendation request, the group recommendation request including user identification of two or more users, where the group recommendation includes geographical location information;

accessing, using the user identification of the two or more users, user profile data for the two or more users and information on third party sources accessed over a second network connection by at least one of the two or more users; and

generating a common recommendation based on the user profile data and the information on the third party sources accessed, the common recommendation including a venue recommendation determined using specialized indexing and retrieval algorithms that identify patterns in the user profile data and the information on the third party sources.

2. The system of claim 1, wherein the geographical location information includes a first geographical location of a first user associated with the device and a second geographical location of a second user from the two or more users identified in the group recommendation request.

3. The system of claim 2, wherein first and second geographical locations and user interactions of the two or more users are used in generating the common recommendation.

4. The system of claim 3, wherein the user interactions include implicit and explicit interactions and wherein the implicit interactions are recorded clicks of the two or more users and explicit interactions are direct inputs of the two or more users.

5. The system of claim 4, wherein the implicit and explicit interactions of the first user are analyzed to extract a first feature matrix and wherein the first feature matrix is projected onto the geographical location of the first user.

6. The system of claim 5, wherein the implicit and explicit interactions of a second user in the two or more users are analyzed to extract a second feature matrix and wherein the second feature matrix is projected onto the geographical location of the second user and where a first place graph is generated using the first feature matrix and a second place graph is generated using the second feature matrix.

7. The system of claim 6, wherein a merge place graph is generated using the first and second place graphs, and wherein the merge place graph is used with the specialized indexing algorithm to generate the common recommendation.

8. A method comprising:

receiving, over a first network connection with a device, a group recommendation request, the group recommendation request including user identification of two or more users, and where the group recommendation includes geographical location information;

accessing, by a processor, using the user identification of the two or more users, user profile data for the two or more users and information on third party sources accessed over a second network connection by at least one of the two or more users; and

generating, by the processor, a common recommendation based on the user profile data and the information on the third party sources accessed, the common recommendation including a venue recommendation determined using specialized indexing and retrieval algorithms that identify patterns in the user profile data and the information on the third party sources.

9. The method of claim 8, wherein the geographical location information includes a first geographical location of a first user associated with the device and a second geographical location of a second user from the two or more used identified in the group recommendation request.

10. The method of claim 9, wherein first and second geographical locations and user interactions of the two or more users are used in generating the common recommendation.

11. The method of claim 10, wherein the user interactions include implicit and explicit interactions and wherein the implicit interactions are recorded clicks of the two or more users and explicit interactions are direct inputs of the two or more users.

12. The method of claim 11, wherein the implicit and explicit interactions of the first user are analyzed to extract a first feature matrix and wherein the first feature matrix is projected onto the geographical location of the first user.

13. The method of claim 12, wherein the implicit and explicit interactions of a second user in the two or more users are analyzed to extract a second feature matrix and wherein the second feature matrix is projected onto the geographical location of the second user and where a first place graph is generated using the first feature matrix and a second place graph is generated using the second feature matrix.

14. The method of claim 13, wherein a merge place graph is generated using the first and second place graphs, and wherein the merge place graph is used with the specialized indexing algorithm to generate the common recommendation.

15. A non-transitory machine-readable medium having instructions stored thereon, the instructions executable to cause performance of operations comprising:

receiving, over a first network connection with a device, a group recommendation request, the group recommendation request including user identification of two or more users, and where the group recommendation includes geographical location information;

accessing, by a processor, using the user identification of the two or more users, user profile data for the two or more users and information on third party sources accessed over a second network connection by at least one of the two or more users; and

generating, by the processor, a common recommendation based on the user profile data and the information on the third party sources accessed, the common recommendation including a venue recommendation determined using specialized indexing and retrieval algorithms that identify patterns in the user profile data and the information on the third party sources.

16. The non-transitory machine-readable medium of claim 15, wherein the geographical location information includes a first geographical location of a first user associated with the device and a second geographical location of a second user from the two or more used identified in the group recommendation request.

17. The non-transitory machine-readable medium of claim 16, wherein first and second geographical locations and user interactions of the two or more users are used in generating the common recommendation.

18. The non-transitory machine-readable medium of claim 17, wherein the user interactions include implicit and explicit interactions and wherein the implicit interactions are recorded clicks of the two or more users and explicit interactions are direct inputs of the two or more users.

19. The non-transitory machine-readable medium of claim 18, wherein the implicit and explicit interactions of the first user are analyzed to extract a first feature matrix and wherein the first feature matrix is projected onto the geographical location of the first user.

20. The non-transitory machine-readable medium of claim 19, wherein the implicit and explicit interactions of a second user in the two or more users are analyzed to extract a second feature matrix and wherein the second feature matrix is projected onto the geographical location of the second user and where a first place graph is generated using the first feature matrix and a second place graph is generated using the second feature matrix.