Abstract: A social networking system presents advertisements and recommendation units to its users. The recommendation units suggest actions for the users to increase their engagement with the social networking system or otherwise interact with other users, while the social networking system receives revenue from advertisers for displaying advertisements based on bid values associated with the advertisements. The social networking system determines values for the advertisements and for the recommendation units, where the values are measured in a comparable fashion. This allows the system to rank and select the advertisements and recommendation units together in a unified auction model. For example, the social networking system uses a pacing value to determine values of recommendation units having a common unit of measurement with expected values of advertisements to the social networking system.

Abstract: In one embodiment, a method includes receiving a request from a first user for a content page; in response to the request, identifying at least one content page, wherein the content page is associated with a page identifier; identifying a plurality of content items based at least in part on a plurality of content features associated with the content page; ranking the plurality of content items based at least in part on a plurality of user features associated with the first user; and delivering to the first user, with the requested content page, one or more of the plurality of content items as recommendations to the first user based on the ranking of the content items.

Abstract: In one embodiment, a system includes one or more computing systems that implement a social networking environment containing a large number of heterogeneous objects type, each of the plurality of object types having varying features, the system implementing a generic object recommendation engine for scoring objects and recommending the objects to users of the social networking system. In particular embodiments, the user and content object features are fed as inputs into a heuristic model that generates an expected value for the content object and user. In particular embodiments, the object recommendation engine includes an online learner that may log a user's actions after the initial impression to determine the relatively degree of interest to the user.

Abstract: As a user of a social networking system views a page that includes information provided by the system, certain types of social interactions are monitored. If an interaction monitored for is detected, at least one recommendation unit is identified to present to user on the page. The recommendation unit is identified based on a description of the interaction. The recommendation unit suggests that the user perform a social interaction in the social networking system. The recommendation unit is transmitted to a device of the user and is presented to the user on the page without having to reload the entire page.

Abstract: Evaluating code is disclosed. A configuration associated with a programming problem is determined. A random input data is generated. The programming problem based at least in part on the determined configuration is provided. An output data that corresponds to the random input data is received. The output data was generated by a code responsive to the programming problem. The output data is evaluated.

Abstract: As a user of a social networking system views a page that includes information provided by the system, certain types of social interactions are monitored. If an interaction monitored for is detected, at least one recommendation unit is identified to present to user on the page. The recommendation unit is identified based on a description of the interaction. The recommendation unit suggests that the user perform a social interaction in the social networking system. The recommendation unit is transmitted to a device of the user and is presented to the user on the page without having to reload the entire page.

Abstract: As a user of a social networking system views a page that includes information provided by the system, certain types of social interactions are monitored. If an interaction monitored for is detected, at least one recommendation unit is identified to present to user on the page. The recommendation unit is identified based on a description of the interaction. The recommendation unit suggests that the user perform a social interaction in the social networking system. The recommendation unit is transmitted to a device of the user and is presented to the user on the page without having to reload the entire page.

Abstract: A social networking system presents advertisements and recommendation units to its users. The recommendation units suggest actions for the users to increase their engagement with the social networking system or otherwise interact with other users, while the social networking system receives revenue from advertisers for displaying advertisements based on bid values associated with the advertisements. The social networking system determines values for the advertisements and for the recommendation units, where the values are measured in a comparable fashion. This allows the system to rank and select the advertisements and recommendation units together in a unified auction model. For example, the social networking system uses a pacing value to determine values of recommendation units having a common unit of measurement with expected values of advertisements to the social networking system.

Abstract: In one embodiment, a system includes one or more computing systems that implement a social networking environment containing a large number of heterogeneous objects type, each of the plurality of object types having varying features, the system implementing a generic object recommendation engine for scoring objects and recommending the objects to users of the social networking system. In particular embodiments, the user and content object features are fed as inputs into a heuristic model that generates an expected value for the content object and user. In particular embodiments, the object recommendation engine includes an online learner that may log a user's actions after the initial impression to determine the relatively degree of interest to the user.

Abstract: As a user of a social networking system views a page that includes information provided by the system, certain types of social interactions are monitored. If an interaction monitored for is detected, at least one recommendation unit is identified to present to user on the page. The recommendation unit is identified based on a description of the interaction. The recommendation unit suggests that the user perform a social interaction in the social networking system. The recommendation unit is transmitted to a device of the user and is presented to the user on the page without having to reload the entire page.

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.