Abstract:
A story describing an activity performed by an interacting user is distributed to viewing users according to the influencer scores for the viewing users. Each influencer score can be calculated based at least in part on the influence of a viewing user on those users connected to the viewing user, and on the influencer scores for the users connected to the viewing user. Based on the determined influencer scores, at least one of the viewing users can be provided with the story describing the activity performed by the interacting user.
Abstract:
A story describing an activity performed by an interacting user is distributed to viewing users according to the influencer scores for the viewing users. Each influencer score can be calculated based at least in part on the influence of a viewing user on those users connected to the viewing user, and on the influencer scores for the users connected to the viewing user. Based on the determined influencer scores, at least one of the viewing users can be provided with the story describing the activity performed by the interacting user.
Abstract:
A story describing an activity performed by an interacting user is distributed to viewing users according to the influencer scores for the viewing users. Each influencer score can be calculated based at least in part on the influence of a viewing user on those users connected to the viewing user, and on the influencer scores for the users connected to the viewing user. Based on the determined influencer scores, at least one of the viewing users can be provided with the story describing the activity performed by the interacting user.
Abstract:
A social networking system presents content items to users, who then provide feedback regarding pairs of content items. The feedback includes a selection of a content item of the pair of content items that was preferred by the user over the other content item. The social networking system uses this information to train a predictive model that scores content items based on quality. The content items may be advertisements. The social networking system uses the pair-wise comparisons of the advertisements to determine feedback coefficients in an advertising quality score prediction model using regression analysis of the pair-wise comparisons for each predictive factor in the model. In this way, the pair-wise comparisons are used to train the prediction model to understand which advertisements are more enjoyable than others. A feedback coefficient for each predictive factor may be computed based on the preferences received from the group of users.
Abstract:
A viewing user is provided with sponsored stories describes actions of a user connected to the viewing user associated with an object promoted by an advertiser or actions otherwise promoted by the advertiser. Based on a performance metric, the social networking system selects the user or action to be described by the sponsored story. For example, the social networking system ranks candidate sponsored stories describing different actions or users and selects a candidate sponsored story to increase the likelihood of a viewing user interacting with the selected candidate sponsored story.
Abstract:
A social networking system receives advertisement requests from advertisers describing information about advertisements and determines one or more ad topics associated with the advertisements. When an advertisement is to be presented to a user, the social networking system determines one or more topics associated with the user from actions performed by the user and identifies candidate advertisements having ad topics matching, or similar to, the topics associated with the user. The topics associated with the user may be determined based on the user's most recent actions. One or more of the candidate advertisements are selected for presentation to the user.
Abstract:
A seed cluster comprising a group of users who share a particular attribute and/or affiliation is determined by a social networking system. For each user of the seed cluster, other users and/or entities connected to the user in the social networking system are retrieved. For each retrieved other user or entity, the social networking system may determine whether the other user or entity exhibits the attribute or affiliation based on a random walk algorithm. A resulting targeting cluster of users and/or entities may be used for targeting advertisements targeting to members. A social networking system may also infer an affiliation for a user based on the user's interaction with a page, application, or entity where other users who interacted with the same page, application, or entity have the same affiliation.
Abstract:
A social networking system presents content items to users, who then provide feedback regarding pairs of content items. The feedback includes a selection of a content item of the pair of content items that was preferred by the user over the other content item. The social networking system uses this information to train a predictive model that scores content items based on quality. The content items may be advertisements. The social networking system uses the pair-wise comparisons of the advertisements to determine feedback coefficients in an advertising quality score prediction model using regression analysis of the pair-wise comparisons for each predictive factor in the model. In this way, the pair-wise comparisons are used to train the prediction model to understand which advertisements are more enjoyable than others. A feedback coefficient for each predictive factor may be computed based on the preferences received from the group of users.
Abstract:
A seed cluster comprising a group of users who share a particular attribute and/or affiliation is determined by a social networking system. For each user of the seed cluster, other users and/or entities connected to the user in the social networking system are retrieved. For each retrieved other user or entity, the social networking system may determine whether the other user or entity exhibits the attribute or affiliation based on a random walk algorithm. A resulting targeting cluster of users and/or entities may be used for targeting advertisements targeting to members. A social networking system may also infer an affiliation for a user based on the user's interaction with a page, application, or entity where other users who interacted with the same page, application, or entity have the same affiliation.
Abstract:
A seed cluster comprising a group of users who share a particular attribute and/or affiliation is determined by a social networking system. For each user of the seed cluster, other users and/or entities connected to the user in the social networking system are retrieved. For each retrieved other user or entity, the social networking system may determine whether the other user or entity exhibits the attribute or affiliation based on a random walk algorithm. A resulting targeting cluster of users and/or entities may be used for targeting advertisements targeting to members. A social networking system may also infer an affiliation for a user based on the user's interaction with a page, application, or entity where other users who interacted with the same page, application, or entity have the same affiliation.