Abstract: Based on prior interactions associated with a user, an online system predicts an amount of interaction by the user with an object associated with an advertisement. Using the predicted amount of user interaction, the online system determines an expected value of presenting the advertisement to the user. The advertisement is ranked among other advertisements based on the expected values associated with the advertisements, and one or more advertisements are selected for presentation to the user based on the ranking. An advertisement may also specify a threshold amount of interaction with an associated object as targeting criteria, so the predicted amount of interaction with the object associated with the advertisement may determine if a user is eligible to be presented with the advertisement.

Abstract: When the social networking system selects an advertisement for presentation to the user by a client device, a content identifier associated with the selected advertisement is stored in an advertisement history associated with the user. When the user performs a conversion event associated with the selected advertisement, the social networking system receives a content identifier and a user identifier from a client device used that performed the conversion event and retrieves the user's advertisement history. If the retrieved advertisement history includes a content identifier for the selected advertisement matching the content identifier received from the client device and a time between the conversion event and providing the selected advertisement to the client device is less than a threshold time period, a fee is determined for presentation of the selected advertisement.

Abstract: An advertising platform calculates bids for advertisements based on the value of a conversion for the advertisement. The advertising platform identifies an impression opportunity for an advertisement request and computes an expected value of the conversion as well as a likelihood of the conversion. The advertising platform computes a bid amount based on the expected conversion value and the likelihood of the conversion. Bids based on the value of the conversion allow advertisers to optimize for the value of each conversion instead of simply the conversion rate.

Abstract: An advertising system predicts advertisement reach for a received advertisement request based on an advertiser-specified bid amount and a specification of a target audience. The system samples the target audience, and for each sampled user of the target audience, accesses a recent impression history to obtain costs or bids associated with recent advertisement impressions. The system compares the advertiser-specified bid amount in the received advertisement request to costs or bid values associated with successful advertisement impressions, for each sampled user, in order to determine whether the received advertisement request would have won a bid auction for each given sampled user to successfully reach each given sampled user. An estimated aggregate reach for the sampled users is computed and extrapolated to the targeted user population to estimate a total reach of the advertisement content for the target audience.

Abstract: An advertising system receives from an advertiser at a social networking system an advertisement request, the advertisement request comprising advertisement content and a specification of a target audience for the advertisement content. The advertising system defines a plurality of bid values for the advertisement request. For each of the plurality of bid values, the advertisement system estimates a corresponding value of advertisement reach for the target audience, for example, by estimating a number of users of the target audience for each of whom the given bid value is expected to have resulted in at least one successful impression. The advertiser is provided a visual representation of a bid-reach landscape representing the estimated plurality of advertisement reach values as a function of the plurality of bid values. The advertising system provides, to the advertiser, one or more recommendations for bid values for which corresponding return-on-investment metrics exceed a specified threshold.

Abstract: Based on prior interactions associated with a user, an online system predicts an amount of interaction by the user with an object associated with an advertisement. Using the predicted amount of user interaction, the online system determines an expected value of presenting the advertisement to the user. The advertisement is ranked among other advertisements based on the expected values associated with the advertisements, and one or more advertisements are selected for presentation to the user based on the ranking. An advertisement may also specify a threshold amount of interaction with an associated object as targeting criteria, so the predicted amount of interaction with the object associated with the advertisement may determine if a user is eligible to be presented with the advertisement.

Abstract: Based on prior interactions associated with a user, an online system predicts an amount of interaction by the user with an object associated with an advertisement. Using the predicted amount of user interaction, the online system determines an expected value of presenting the advertisement to the user. The advertisement is ranked among other advertisements based on the expected values associated with the advertisements, and one or more advertisements are selected for presentation to the user based on the ranking. An advertisement may also specify a threshold amount of interaction with an associated object as targeting criteria, so the predicted amount of interaction with the object associated with the advertisement may determine if a user is eligible to be presented with the advertisement.

Abstract: When the social networking system selects an advertisement for presentation to the user by a client device, a content identifier associated with the selected advertisement is stored in an advertisement history associated with the user. When the user performs a conversion event associated with the selected advertisement, the social networking system receives a content identifier and a user identifier from a client device used that performed the conversion event and retrieves the user's advertisement history. If the retrieved advertisement history includes a content identifier for the selected advertisement matching the content identifier received from the client device and a time between the conversion event and providing the selected advertisement to the client device is less than a threshold time period, a fee is determined for presentation of the selected advertisement.

Abstract: A group of social networking system users are associated with a holdout group for an advertisement. Users in the holdout group are not presented with the advertisement. When the advertisement is selected for presentation to a user, the social networking system presents the advertisement to the user if the user is not in the holdout group. However, if the user is in the holdout group, alternative content is presented to the user. If a user performs a conversion event associated with the advertisement via a client device, the social networking system determines a fee for an advertiser if the advertisement was presented to the user. The fee may be adjusted based on differences between conversion events by users in the holdout group for the advertisement and by users not in the holdout group.

Abstract: A social networking system may identify a first set of users as part of a training cluster and identify a second set of users that is similar to the first set of users for purposes of targeting advertisements related to the advertiser. Using past engagement history (e.g., click-through rates), demographic information, and keywords associated with the training cluster of users, a social networking system may generate a training model specific to the training cluster. Confidence scores may be used to identify similar users across the total population of users of the social networking system for creating a targeting cluster of users for the advertisement. A revenue sharing scheme may be used induce page administrators to increase their fan base by enabling advertisers to target advertisements to users that have expressed interest in pages associated with the page administrators.

Abstract: In a social networking system, users may perform different types of actions with respect to objects (e.g., for a song, a user can buy, listen to, recommend, or like the song). Objects on the social networking system may include object properties (e.g., for a song, an artist, genre, and album are the song's object properties). Each type of action performed on a type of object may be aggregated in a different way. Actions may be performed on objects by users through the social networking system, a third-party application installed on the social networking system, and systems external to the social networking system. An advertiser may specify targeting criteria based on multiple different action types and aggregations of an action with respect to a particular object. Users may be categorized based on their interactions with objects having a common element. Ads may then be targeted to the categorized users.

Abstract: In a social networking system, users may perform different types of actions with respect to objects (e.g., for a song, a user can buy, listen to, recommend, or like the song). Objects on the social networking system may include object properties (e.g., for a song, an artist, genre, and album are the song's object properties). Each type of action performed on a type of object may be aggregated in a different way. Actions may be performed on objects by users through the social networking system, a third-party application installed on the social networking system, and systems external to the social networking system. An advertiser may specify targeting criteria based on multiple different action types and aggregations of an action with respect to a particular object. Users may be categorized based on their interactions with objects having a common element. Ads may then be targeted to the categorized users.