INSTANTANEOUS RECOMMENDATION OF SOCIAL INTERACTIONS IN A SOCIAL NETWORKING SYSTEM

• US 20130080524A1
• Filed: 09/28/2011
• Published: 03/28/2013
• Est. Priority Date: 09/28/2011
• Status: Active Application

• Current Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Original Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Inventors: Rubinstein, Yigal Dan, Schoen, Kent, Shi, Yanxin, Klots, Levy, Jin, Robert Kang-Xing, Goder, Andrey, Ye, David Dawei, Spiridinov, Alexey, Narayanan, Srinivas P.
• Application Number:
  US13/247,832
• Publication Number:
  US 20130080524A1
• Time in Patent Office: Days
• US Class Current: 709/204
• CPC Class Codes: G06Q 10/101 Collaborative creation, e.g. joint development of products or services
• CPC Class Codes: G06Q 30/0282 Rating or review of business operators or products

Abstract

When a social interaction by a user in a social networking system is detected, a description of the interaction is created. A service level auction is performed to select one or more service modules to provide recommendation units from a plurality of service modules. Each of the plurality of service modules is configured to provide recommendation units that suggest that the user engage in a social interaction in the social networking system. The description of the interaction is provided to each service module selected and recommendation units are requested. A plurality of recommendation units are received from the selected service modules. A unit level auction is performed to select one of more recommendation units to present to the user from the plurality of recommendation units. The selected recommendation units are transmitted to a device of the user for presentation.

Claims

1. A computer-implemented method comprising:

identifying a user of a social networking system;

selecting one or more services from a plurality of services, wherein each of the plurality of services is configured to provide recommendation units suggesting that the user engage in a social interaction in the social networking system, the services selected based on a prediction of which services are likely to provide recommendation units that are of interest to the user;

requesting recommendation units from each of the selected services;

receiving, from the selected services, a plurality of recommendation units;

selecting one or more recommendation units to present to the user from the plurality of recommendation units; and

transmitting the selected recommendation units for presentation to the user.

2. The method of claim 1, wherein selecting the one or more services further comprises:

calculating a score for each of the plurality of services; and

selecting the one or more services based on the calculated scores.

3. The method of claim 2, wherein the score of a service is calculated based on a likelihood that the user will convert on recommendation units provided by the service.

4. The method of claim 2, wherein the score of a service is determined based on a probabilistic mode that predicts a likelihood that the user will convert on recommendation units provided by the service.

5. The method of claim 2, wherein the score of a service is determined based on a machine learned model that predicts a likelihood that the user will convert on recommendation units provided by the service.

6. The method of claim 2, wherein the score of a service is calculated based on how valuable it is to the social networking system for the user to convert on recommendation units provided by the service.

7. The method of claim 1, wherein a selected service determines which recommendation units to provide based on a description of a detected social interaction by the user in the social networking system.

8. The method of claim 1, wherein selecting the one or more recommendation units further comprises:

calculating a score for each of the plurality of recommendation units; and

selecting the one or more recommendation units based on the calculated scores.

9. The method of claim 8, wherein the score of a recommendation unit is calculated based on a likelihood that the user will convert on the recommendation unit.

10. The method of claim 8, wherein the score of a recommendation unit is determined based on a probabilistic model that predicts a likelihood that the user will convert on the recommendation unit.

11. The method of claim 8, wherein the score of a recommendation unit is determined based on a machine learned model that predicts a likelihood that the user will convert on the recommendation unit.

12. The method of claim 8, wherein the score of a recommendation unit is calculated based on how valuable it is to the social networking system for the user to convert on the recommendation unit.

13. A computer-implemented method comprising:

identifying a user of a social networking system;

performing a service level auction to select one or more services from a plurality of services, wherein each service is configured to provide recommendation units suggesting that the user engage in a social interaction in the social networking system;

requesting recommendation units from each of the selected services;

receiving, from the selected services, a plurality of recommendation units;

performing a unit level auction to select one or more recommendation units to present to the user from the plurality of recommendation units; and

transmitting the selected recommendation units for presentation to the user.

14. The method of claim 13, wherein performing the service level auction further comprises:

calculating a score for each of the plurality of services; and

selecting the one or more services based on the calculated scores.

15. The method of claim 14, wherein the score of a service is calculated based on a likelihood that the user will convert on recommendation units provided by the service.

16. The method of claim 14, wherein the score of a service is determined based on a probabilistic model that predicts a likelihood that the user will convert on recommendation units provided by the service.

17. The method of claim 14, wherein the score of a service is calculated based on how valuable it is to the social networking system for the user to convert on recommendation units provided by the service.

18. The method of claim 13, wherein performing a unit level auction further comprises:

calculating a score for each of the plurality of recommendation units; and

selecting the one or more recommendation units based on the calculated scores.

19. The method of claim 18, wherein the score of a recommendation unit is calculated based on a likelihood that the user will convert on the recommendation unit.

20. The method of claim 18, wherein the score of a recommendation unit is calculated based on how valuable it is to the social networking system for the user to convert on the recommendation unit.