Systems and methods for generating location-based group recommendations

• US 9,251,536 B2
• Filed: 06/10/2013
• Issued: 02/02/2016
• Est. Priority Date: 12/07/2011
• Status: Active Grant

• Current Assignee: PayPal, Inc. (PayPal Holdings, Inc.)
• Original Assignee: eBay Inc.
• Inventors: Caralis, Jim, Kogan, Nataly, Nakamura, Masumi, Mastroianni, Michael, Sundram, Jason
• Primary Examiner(s): PATEL, MAHENDRA R
• Application Number:
  US13/913,768
• Publication Number:
  US 20130346233A1
• Time in Patent Office: 967 Days
• Field of Search: 455/456.3, 455/414.1, 455/456.1, 375/240.24, 705/26.1, 705/14.53
• 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

1. A method for providing location-aware group recommendations, the method comprising:

receiving, at a network-based system, a group recommendation request from a mobile device associated with a first user, the group recommendation request including identification of a second user, a relationship indicator that includes information describing a relationship between the first user and the second user, and information identifying a current location associated with at least one of the first and second users;

accessing, using one or more processors within the network-based system, a first place graph associated with the first user and a second place graph associated with the second user; and

generating, using one or more processors within the network-based system, a predictive common recommendation, the predictive common recommendation generated based on calculations performed on the first and second place graphs.

2. The method of claim 1, wherein the accessing the second place graph associated with the second user includes using the relationship indicator.

3. The method of claim 1, wherein the relationship indicator indicates that the second user is part of a social graph associated with the first user.

4. The method of claim 1, wherein the relationship indicator indicates that the second user exchanged user profile data directly with the first user, the user profile data including the second place graph.

5. The method of claim 4, wherein the relationship indicator indicates that the first user and the second user exchanged the user profile data via mobile devices associated with the first user and the second user.

6. The method of claim 1, wherein the generating a predictive common recommendation includes:

updating the first place graph for the first user and the second place graph for the second user based on the current location;

merging the first place graph and the second place graph into a third place graph; and

traversing the third place graph to generate the predictive common recommendation.

7. The method of claim 6, wherein updating the first place graph or the second place graph includes:

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

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

receiving a group recommendation request from a mobile device associated with a first user, the group recommendation request including identification of a second user, a relationship indicator that includes information describing a relationship between the first user and the second user, and information identifying a current location associated with at least one of the first and second users;

accessing a first place graph associated with the first user and a second place graph associated with the second user; and

generating a predictive common recommendation, the predictive common

recommendation generated based on calculations performed on the first and second place graphs.

9. The non-transitory machine-readable storage medium of claim 8, wherein the accessing the second place graph associated with the second user includes using the relationship indicator.

10. The non-transitory machine-readable storage medium of claim 8, wherein the relationship indicator indicates that the second user is part of a social graph associated with the first user.

11. The non-transitory machine-readable storage medium of claim 8, wherein the relationship indicator indicates that the second user exchanged user profile data directly with the first user, the user profile data including the second place graph.

12. The non-transitory machine-readable storage medium of claim 11, wherein the relationship indicator indicates that the first user and the second user exchanged the user profile data via mobile devices associated with the first user and the second user.

13. The non-transitory machine-readable storage medium of claim 8, wherein the generating a predictive common recommendation includes:

updating the first place graph for the first user and the second place graph for the second user based on the current location;

merging the first place graph and the second place graph into a third place graph; and

traversing the third place graph to generate the predictive common recommendation.

14. The non-transitory machine-readable storage medium of claim 13, wherein updating the first place graph or the second place graph includes:

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

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.

15. A network-based system for generating location-aware group recommendations, the system comprising:

a server communicatively connected to a network, the server including one or more processors and a memory device, the memory device including instructions that, when executed by the one or more processors, cause the server to perform operations including;

receiving a group recommendation request from a mobile device associated with a first user, the group recommendation request including identification of a second user, a relationship indicator that includes information describing a relationship between the first user and the second user, and information identifying a current location associated with at least one of the first and second users;

accessing a first place graph associated with the first user and a second place graph associated with the second user; and

generating a predictive common recommendation, the predictive common recommendation generated based on calculations performed on the first and second place graphs.

16. The network-based system of claim 15, wherein the generating a predictive common recommendation includes:

updating the first place graph for the first user and the second place graph for the second user based on the current location;

merging the first place graph and the second place graph into a third place graph; and

traversing the third place graph to generate the predictive common recommendation.

17. The network-based system of claim 16, wherein updating the first place graph or the second place graph includes:

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

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.