Abstract:
A social networking system dynamically adjusts a number of advertisements presented to a user along with organic content items by modifying a ranking including organic content items and advertisements. Partial engagement scores are generated for organic content items based on an expected amount of user interaction with each organic content item, and scores are generated for advertisements based on expected user interaction and bid amounts associated with each organic content item. An engagement score measuring the user's estimated interaction with a content feed including organic content items without advertisements and an additional engagement score measuring the user's estimated interaction with a content feed including organic content items and advertisements are determined from the partial engagement scores and the scores. A difference between the additional engagement score and the engagement score modifies a conversion factor used to combine expected user interaction and bid amounts to generate advertisement scores.
Abstract:
A social networking system dynamically adjusts a number of advertisements presented to a user along with organic content items by modifying a ranking including organic content items and advertisements. Partial engagement scores are generated for organic content items based on an expected amount of user interaction with each organic content item, and scores are generated for advertisements based on expected user interaction and bid amounts associated with each organic content item. An engagement score measuring the user's estimated interaction with a content feed including organic content items without advertisements and an additional engagement score measuring the user's estimated interaction with a content feed including organic content items and advertisements are determined from the partial engagement scores and the scores. A difference between the additional engagement score and the engagement score modifies a conversion factor used to combine expected user interaction and bid amounts to generate advertisement scores.
Abstract:
Systems, methods, and non-transitory computer-readable media can determine one or more respective topics of interest for at least some users of a social networking system. At least some of the topics can be propagated to at least a first user, wherein the propagated topics were determined to be of interest to users that follow the first user in the social networking system. At least one topic from the propagated topics for which the first user is a topical authority is determined.
Abstract:
An online system sells fixed-price advertising guaranteeing a number of impressions or a number of actions associated with an advertisement by users of the online system. The price for an advertisement associated with a guaranteed number of impressions or actions is based on a target bid amount for selecting the advertisement from a group of advertisements using a conventional pricing scheme and a predicted likelihood that the guaranteed number of impressions or actions occur. The price may be further adjusted by a premium that accounts for a risk of revenue lost by the online system for displaying an advertisement associated with a guaranteed number of impressions or a number of actions rather than conventionally-priced advertisements.
Abstract:
A social networking system dynamically adjusts a number of advertisements presented to a user along with organic content items by modifying a ranking including organic content items and advertisements. Partial engagement scores are generated for organic content items based on an expected amount of user interaction with each organic content item, and scores are generated for advertisements based on expected user interaction and bid amounts associated with each organic content item. An engagement score measuring the user's estimated interaction with a content feed including organic content items without advertisements and an additional engagement score measuring the user's estimated interaction with a content feed including organic content items and advertisements are determined from the partial engagement scores and the scores. A difference between the additional engagement score and the engagement score modifies a conversion factor used to combine expected user interaction and bid amounts to generate advertisement scores.
Abstract:
A social networking system dynamically adjusts a number of advertisements presented to a user along with organic content items by modifying a ranking including organic content items and advertisements. Partial engagement scores are generated for organic content items based on an expected amount of user interaction with each organic content item, and scores are generated for advertisements based on expected user interaction and bid amounts associated with each organic content item. An engagement score measuring the user's estimated interaction with a content feed including organic content items without advertisements and an additional engagement score measuring the user's estimated interaction with a content feed including organic content items and advertisements are determined from the partial engagement scores and the scores. A difference between the additional engagement score and the engagement score modifies a conversion factor used to combine expected user interaction and bid amounts to generate advertisement scores.
Abstract:
Systems, methods, and non-transitory computer-readable media can determine one or more respective topics of interest for at least some users of a social networking system. At least some of the topics can be propagated to at least a first user, wherein the propagated topics were determined to be of interest to users that follow the first user in the social networking system. At least one topic from the propagated topics for which the first user is a topical authority is determined.
Abstract:
An online system penalizes content items having features matching features of additional content items previously presented to a user within a specified time interval. The online system identifies various features of the content item and identifies features of content items previously presented to the user within the specified time interval. Feature penalties are determined for various features of the content item based on a number of previously presented content items having a common feature with the content item. Weights may be associated with various content items having a feature matching a feature of the content item based on a time between presentation of the previously presented content item and a current time. A penalty for the content item is determined based on the feature penalties for the features of the content item, and the penalty is applied to a bid amount associated with the content item.