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Patent US9984378
Issued 2018-05-29
Passive Sharing
A social networking system leverages information obtained regarding user interactions with applications hosted through the social networking system as well as information obtained regarding purchases of application-related digital items and digital currencies to provide insight into user behavior. The social networking system can use these insights to enhance the user's experience of the applications, purchases, and the social networking system generally. For example, the social networking system can customize transaction flows, provide credit referrals, and provide users with aggregated yet personally tailored sales information.
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- 1. A method comprising:
storing in a social networking system a plurality of user profiles for a plurality of users, the user profiles comprising connection information indicating connections between the users of the social networking system as well as histories of application use by users of applications hosted by third party systems and presented on a same interface as the social networking system; receiving transaction information for the users, the transaction information identifying items purchased and the users who purchased the items, the items being able to be used within the applications; aggregating, by a computer processor of the social networking system, the transaction information for items purchased by a subset of the users sharing a direct connection with a user of the social networking system; determining, by the computer processor, an item of interest to the user to be used in conjunction with an application of the applications based on the aggregated transaction information and based on whether the user's purchase history indicates that the user has previously purchased items to be used in conjunction with the application; sending the aggregated transaction information regarding the item of interest to the user; and responsive to the user purchasing items to be used in conjunction with an application:
determining one or more users of the subset of users to be provided with bonus credits; and
sending a list of the one or more users of the subset of users describing potential recipients of bonus credits based on the aggregated transaction information, wherein the one or more users are determined based on a predicted likelihood by a machine learned algorithm that the one or more users will purchase additional credits if awarded bonus credits.
- 8. A computer comprising:
a processor for executing computer program modules; and a non-transitory computer-readable storage medium storing executable computer program modules comprising:
a user profile store configured to store in a social networking system a plurality of user profiles for a plurality of users, the user profiles comprising connection information indicating connections between the users of the social networking system as well as histories of application use by users of applications hosted by third party systems and presented on a same interface as the social networking system;
a web server configured to receive transaction information for the users, the transaction information identifying items purchased and the users who purchased the items, the items being able to be used within the applications; and
a credit engine configured to:
aggregate the transaction information for items purchased by a subset of the users sharing a direct connection with a user of the social networking system;
determine an item of interest to the user to be used in conjunction with an application of the applications based on the aggregated transaction information and based on whether the user's purchase history indicates that the user has previously purchased items to be used in conjunction with the application;
send the aggregated transaction information regarding the digital item of interest to the user; and
responsive to the user purchasing items to be used in conjunction with an application:
determine one or more users of the subset of users to be provided with bonus credits; and
send a list of the one or more users of the subset of users describing potential recipients of bonus credits based on the aggregated transaction information, wherein the one or more users are determined based on a predicted likelihood by a machine learned algorithm that the one or more users will purchase additional credits if awarded bonus credits.
