Abstract:
An analysis system analyzes known user affinities to identify particular objects that serve as useful predictors of whether a given user will have an affinity for a given topic, even if the user has not previously expressly specified an affinity for that topic. Specifically, both the topic group of users associated with a given topic and the category group of users associated with the topic's more general category are identified. For each of a set of objects, degrees of divergence between the topic group and the category group are evaluated for a criterion evaluated with respect to the object. A topic profile is created based on objects for which there is a high degree of divergence.
Abstract:
An online system receives information from an entity identifying a set of users of the online system and groups users included in the set into clusters based on their similarities using a clustering model or algorithm (e.g., k-means clustering) and based on one or more parameters specified by the entity. The online system generates expanded clusters that include additional users in one or more clusters based on similarities between the additional users and users in various clusters. If an additional user is included in multiple expanded clusters, the online assigns the additional user exclusively to an expanded cluster that best fits the user.
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 maintains characteristics with its users, with various characteristics, such as age, specified by the users (i.e., “asserted characteristics”). The social networking system selects content for a user based at least in part on the characteristics associated with the user. To account for potential inaccuracies in an asserted age of a user, the social networking system clusters users based on ages of other users connected to users. The online system receives verified ages for users in a cluster from a trusted third party system that maintains more accurate characteristics for users than the social networking system. By comparing the asserted ages for users in the cluster to the verified ages for users in the cluster, the social networking system determines an accuracy of the asserted ages for users in the cluster. The accuracy may be used when selecting content for the users.
Abstract:
An online system receives a video-presentation request from a third party system. The video-presentation request comprises a video and a target audience specification for the video. The online system selects a plurality of users as the target audience of the video based on the target audience specification. From the target audience, the online system generates a sample subset of users and determines a sampled video reach count for the subset of users. A sample user who, as determined by the online system, would have viewed the view for at least a threshold duration, is included in the sampled video view count. The online system estimates a total video reach count for target audience by extrapolating the sampled video reach count for the subset of users to the target audience. The total video reach count can be used to determine a parameter for presenting the video on an online system.
Abstract:
An online system receives a video-presentation request from a third party system. The video-presentation request comprises a video and a target audience specification for the video. The online system selects a plurality of users as the target audience of the video based on the target audience specification. From the target audience, the online system generates a sample subset of users and determines a sampled video reach count for the subset of users. A sample user who, as determined by the online system, would have viewed the view for at least a threshold duration, is included in the sampled video view count. The online system estimates a total video reach count for target audience by extrapolating the sampled video reach count for the subset of users to the target audience. The total video reach count can be used to determine a parameter for presenting the video on an online system.
Abstract:
An online system scores campaign audiences based on historical scoring data for similar audiences. A third party system selects a target audience and a day on which the target audience should be exposed to a campaign. The online system generates an availability grid and a score grid to determine a score for the target audience. Values in the availability grid are determined based on the availability of exposure time for the target audience on the specified date. Values in the score grid are based on historical scoring data for the same audience. The online system scores the target audience by interpolating between data points in the score grid based on a selected availability from the availability grid.
Abstract:
A system forms sub-groups from a given user group of a social networking system and form descriptions of the sub-groups that provide an intuitive understanding of sub-group composition, such as likings of the sub-groups. In one embodiment, a given user group of a social networking system is clustered into a plurality of sub-groups, and representative characteristics—such as the characteristics of a composite or actual member of the sub-group—are determined for each sub-group. In order to form sub-group descriptions, a set of objects, such as pages of the social networking system, is ranked with respect to the representative characteristics of the sub-group. The highest-ranking objects for a sub-group are then used to form the description of that sub-group. For example, the topics associated with each of the highest-ranking pages can be combined into the sub-group description.
Abstract:
An advertiser specifies an advertising campaign including one or more targeting criteria for presentation to users of an online system, which retrieves information describing previously completed advertisement auctions for presenting advertisement to users of the online system satisfying one or more of the targeting criteria. Based on the retrieved information, the online system associates various bid amounts with the advertising campaign and determines the advertising campaign's estimated performance for various bid amounts. For each bid amount, the online system determines a number of previously completed advertisement auctions that would have selected an advertisement from the advertising campaign, an amount that would have been charged to the advertiser if an advertisement campaign was selected, and a number of distinct users that would have been presented with an advertisement from the advertising campaign.
Abstract:
A system forms sub-groups from a given user group of a social networking system and form descriptions of the sub-groups that provide an intuitive understanding of sub-group composition, such as likings of the sub-groups. In one embodiment, a given user group of a social networking system is clustered into a plurality of sub-groups, and representative characteristics—such as the characteristics of a composite or actual member of the sub-group—are determined for each sub-group. In order to form sub-group descriptions, a set of objects, such as pages of the social networking system, is ranked with respect to the representative characteristics of the sub-group. The highest-ranking objects for a sub-group are then used to form the description of that sub-group. For example, the topics associated with each of the highest-ranking pages can be combined into the sub-group description.