SYSTEMS AND METHODS TO DETERMINE LOCATION RECOMMENDATIONS

• US 20170180493A1
• Filed: 12/16/2015
• Published: 06/22/2017
• Est. Priority Date: 12/16/2015
• Status: Active Grant

• Current Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Original Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Inventors: Frederick, Adriel Samuel
• Granted Patent: US 10,455,031 B2
• Time in Patent Office: Days
• CPC Class Codes: G06Q 50/01 Social networking
• CPC Class Codes: H04L 67/306 User profiles
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34
• CPC Class Codes: H04W 4/21 for social networking applications

Abstract

Systems, methods, and non-transitory computer readable media are configured to receive ratings for a plurality of locations associated with a location type. The ratings are processed to develop a personalized model for a user to identify candidate locations for the user. At least one candidate location is provided as a recommendation for the user based on the personalized model.

Claims

1. A computer-implemented method comprising:

receiving, by a computing system, ratings for a plurality of locations associated with a location type;

processing, by the computing system, the ratings to develop a personalized model for a user to identify candidate locations for the user; and

providing, by the computing system, at least one candidate location as a recommendation for the user based on the personalized model.

2. The computer-implemented method of claim 1, wherein the location type relates to at least one of restaurants, stores, schools, bars, companies, congregations, and destinations.

3. The computer-implemented method of claim 1, further comprising:

for each respective location of the plurality of locations, determining attribute values of each attribute associated with the location type.

4. The computer-implemented method of claim 3, wherein at least a portion of the attribute values are determined based at least in part on user data maintained by a social networking system.

5. The computer-implemented method of claim 3, wherein the processing the ratings further comprises:

correlating the ratings for the plurality of locations with the attribute values for each respective location of the plurality of locations;

identifying attribute values that are desired by the user based on the correlating; and

developing the personalized model to reflect the attribute values that are desired by the user.

6. The computer-implemented method of claim 5, wherein the processing the ratings further comprises:

assigning a weight to each attribute value to reflect an importance of the attribute value to the user; and

developing the personalized model to reflect the assigned weight.

7. The computer-implemented method of claim 1, wherein the receiving ratings further comprises:

receiving a rating for at least a first location from the user;

receiving ratings for at least the first location from a set of other users; and

receiving ratings for a second location from the set of other users.

8. The computer-implemented method of claim 7, wherein the processing the ratings further comprises:

calculating a pairwise difference between the rating for at least the first location from the user and each rating for at least the first location from each user in the set of other users to generate difference values; and

combining the difference values to generate a confidence interval.

9. The computer-implemented method of claim 8, wherein the processing the ratings further comprises:

calculating an expected rating for the second location for the user based on the ratings for the second location from the set of other users; and

applying the confidence interval to the expected rating to generate an expected rating interval.

10. The computer-implemented method of claim 9, wherein the processing the ratings further comprises:

receiving an actual rating for the second location from the user;

determining that the actual rating does not fall within the expected rating interval; and

developing the personalized model based on additional ratings for additional locations from the user and the set of other users.

11. A system comprising:

at least one processor; and

a memory storing instructions that, when executed by the at least one processor, cause the system to perform;

receiving ratings for a plurality of locations associated with a location type;

processing the ratings to develop a personalized model for a user to identify candidate locations for the user; and

providing at least one candidate location as a recommendation for the user based on the personalized model.

12. The system of claim 11, wherein the location type relates to at least one of restaurants, stores, schools, bars, companies, congregations, and destinations.

13. The system of claim 11, further comprising:

for each respective location of the plurality of locations, determining attribute values of each attribute associated with the location type.

14. The system of claim 13, wherein the processing the ratings further comprises:

correlating the ratings for the plurality of locations with the attribute values for each respective location of the plurality of locations;

identifying attribute values that are desired by the user based on the correlating; and

developing the personalized model to reflect the attribute values that are desired by the user.

15. The system of claim 14, wherein the processing the ratings further comprises:

assigning a weight to each attribute value to reflect an importance of the attribute value to the user; and

developing the personalized model to reflect the assigned weight.

16. A non-transitory computer-readable storage medium including instructions that, when executed by at least one processor of a computing system, cause the computing system to perform a method comprising:

receiving ratings for a plurality of locations associated with a location type;

processing the ratings to develop a personalized model for a user to identify candidate locations for the user; and

providing at least one candidate location as a recommendation for the user based on the personalized model.

17. The non-transitory computer-readable storage medium of claim 16, wherein the location type relates to at least one of restaurants, stores, schools, bars, companies, congregations, and destinations.

18. The non-transitory computer-readable storage medium of claim 16, further comprising:

for each respective location of the plurality of locations, determining attribute values of each attribute associated with the location type.

19. The non-transitory computer-readable storage medium of claim 18, wherein the processing the ratings further comprises:

correlating the ratings for the plurality of locations with the attribute values for each respective location of the plurality of locations;

identifying attribute values that are desired by the user based on the correlating; and

developing the personalized model to reflect the attribute values that are desired by the user.

20. The non-transitory computer-readable storage medium of claim 19, wherein the processing the ratings further comprises:

assigning a weight to each attribute value to reflect an importance of the attribute value to the user; and

developing the personalized model to reflect the assigned weight.