SYSTEMS AND METHODS FOR GENERATING LOCATION-BASED GROUP RECOMMENDATIONS

• US 20160148299A1
• Filed: 02/01/2016
• Published: 05/26/2016
• 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
• Granted Patent: US 9,552,605 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• 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

11. A non-transitory machine-readable storage medium comprising instructions which, when performed by a network-based system, cause the system to:

identify a current location of a first user;

receive information about a location of a second user;

generate a first place graph based on the current location of the first user and implicit interactions of the first user from the user profile data of the first user, the first place graph including a first set of one or more nodes representing physical locations;

generate a second place graph based on the location of the second user and implicit interactions of the second user from the user profile data of the second user, the second place graph including a second set of one or more nodes representing physical locations;

merge the first place graph and the second place graph to generate a third place graph, the third place graph including a third set of one or more nodes;

generate a predictive common recommendation by traversing the third place graph.

1. A method comprising:

identifying a current location of a first user;

receiving information about a location of a second user;

generating a first place graph based on the current location of the first user and implicit interactions of the first user from the user profile data of the first user, the first place graph including a first set of one or more nodes representing physical locations;

generating a second place graph based on the location of the second user and implicit interactions of the second user from the user profile data of the second user, the second place graph including a second set of one or more nodes representing physical locations;

merging the first place graph and the second place graph to generate a third place graph, the third place graph including a third set of one or more nodes;

generating a predictive common recommendation by traversing the third place graph.

2. The method of claim 1, wherein the receiving the information about the location of the second user includes receiving a relationship indicator that includes information describing a relationship between the first user and the second user.

3. The method of claim 2, wherein the relationship indicator indicates that the second user is part of a social network associated with the first user.

4. The method of claim 2, wherein the relationship indicator indicates that the second user exchanged user profile data directly with the first user.

5. The method of claim 1, wherein the predictive common recommendation is based on the user profile data of the first user and the user profile data of the second user.

6. The method of claim 1, wherein the implicit interactions include search queries of the first user and the second user.

7. The method of claim 1, wherein calculating the first place graph includes:

accessing the user profile data of the first user, the user profile data including a first plurality of places with associated interaction history recorded within the user profile data of the first user;

extracting a feature matrix from the first plurality of places;

accessing place data for a second plurality of places within the current location; and

projecting the feature matrix from the first plurality of places onto the second plurality of places within the current location.

8. The method of claim 1, wherein calculating the second place graph includes:

accessing the user profile data of the second user, the user profile data including a first plurality of places with associated interaction history recorded within the user profile data of the second user;

extracting a feature matrix from the first plurality of places;

accessing place data for a second plurality of places within the current location; and

projecting the feature matrix from the first plurality of places onto the second plurality of places within the current location.

9. The method of claim 1, wherein the receiving information about the location of the second user includes receiving a selection of the second user from a plurality of users, the plurality of users associated with the first user through a social graph.

10. The method of claim 1, wherein the receiving information about the location of the second user includes detecting a second mobile device associated with the second user in proximity to a first mobile device of the first user.

12. The non-transitory machine readable storage medium of claim 11, wherein the instructions to receive information about the location of the second user cause the machine to perform operations further comprising:

receive a relationship indicator that includes information describing a relationship between the first user and the second user.

13. The non-transitory machine readable storage medium of claim 12, wherein the relationship indicator indicates that the second user is part of a social network associated with the first user.

14. The non-transitory machine readable storage medium of claim 12, wherein the relationship indicator indicates that the second user exchanged user profile data directly with the first user.

15. The non-transitory machine readable storage medium of claim 11, wherein the predictive common recommendation is based on the user profile data of the first user and the user profile data of the second user.

16. The non-transitory machine readable storage medium of claim 11, wherein the implicit interactions include search queries of the first user and the second user.

15-1. The non-transitory machine readable storage medium of claim 11, wherein the instructions to calculate the first place graph cause the system to:

access the user profile data of the first user, the user profile data including a first plurality of places with associated interaction history recorded within the user profile data of the first user;

extract a feature matrix from the first plurality of places;

access place data for a second plurality of places within the current location; and

project the feature matrix from the first plurality of places onto the second plurality of places within the current location.

16-1. The non-transitory machine readable storage medium of claim 11, wherein the instructions to calculate the second place graph cause the system to:

access the user profile data of the second user, the user profile data including a first plurality of places with associated interaction history recorded within the user profile data of the second user;

extract a feature matrix from the first plurality of places;

access place data for a second plurality of places within the current location; and

project the feature matrix from the first plurality of places onto the second plurality of places within the current location.

17. A system comprising:

processors; and

a memory storing instructions that, when executed by at least one processor among the processors, causes the system to perform operations comprising;

identifying a current location of a first user;

receiving information about a location of a second user;

generating a first place graph based on the current location of the first user and implicit interactions of the first user from the user profile data of the first user, the first place graph including a first set of one or more nodes representing physical locations;

generating a second place graph based on the location of the second user and implicit interactions of the second user from the user profile data of the second user, the second place graph including a second set of one or more nodes representing physical locations;

merging the first place graph and the second place graph to generate a third place graph, the third place graph including a third set of one or more nodes;

generating a predictive common recommendation by traversing the third place graph.

18. The system of claim 17, wherein the receiving the information about the location of the second user includes receiving a relationship indicator that includes information describing a relationship between the first user and the second user.

19. The system of claim 18, wherein the relationship indicator indicates that the second user is part of a social network associated with the first user.

20. The system of claim 18, wherein the relationship indicator indicates that the second user exchanged user profile data directly with the first user.