INSTANTANEOUS RECOMMENDATION OF SOCIAL INTERACTIONS IN A SOCIAL NETWORKING SYSTEM
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.
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.