Abstract:
An online system presenting content items to a user generates a model that predicts a latent metric describing user actions that occur at least a reasonable amount of time after presentation of content items. To determine the latent metric, the online system retrieves one or more models predicting likelihoods of the user performing various interactions when presented with the content items and determines weights associated with different retrieved models. Combining the weighted retrieved models generates a model for determining the latent metric. As the retrieved models are based on data accessible to the online system in less than the reasonable amount of time after presenting content items, weighing the retrieved models allows the online system to predict the latent metric describing user actions occurring after content items are presented. When selecting content items for the user, the online system accounts for the latent metric determined by the generated model.
Abstract:
An online system receives a sponsored content item including a maximum amount of compensation for accessing the content, a budget, and a tracking mechanism identifying an action. When an opportunity to present sponsored content to a user eligible to be presented with the sponsored content item is identified, the online system determines a likelihood of the user performing the action identified by the tracking mechanism and an average likelihood of other users performing the action identified by the tracking mechanism. Based on the determined likelihood and the average likelihood, the online system determines a subsidy value. Additionally, the online system generates a penalty value inversely proportional to a number of the identified action that have been identified. The online system increases a bid amount by the subsidy value decreases the bid amount by the penalty value to determine whether to present the sponsored content item to the user.
Abstract:
A content delivery system adjusts bids across publishing channels to account for historical ratios that content was targeted to a content item's audience. A content campaign for users of a content delivery system is devised for two or more sponsored content providers. Targeting criteria for the content item is used to define an audience for the content item, and a sample group from that audience is chosen. The ratio of content impressions among the content providers is identified for the sample group among prior content presentations to these users. For the content item, based on the current ratio of presenting content across a secondary publishing channel and a benchmark publishing channel, a channel control factor is adjusted for the secondary publishing channel based on a numerical comparison of these two ratios. This adjusted channel control factor adjusts the bid price per impression for displaying content by the content campaign.
Abstract:
An online system determines a change in of revenue received when a budget for presenting content received from a publishing user is increased. Based on the change in revenue to the online system, the online system determines a subsidy that the online system can provide to one or more publishing users that increases revenue received by the online system from the publishing users, while accounting for the subsidy. To determine the change in revenue for determining the subsidy, the online system selects a subset of publishing users, increases budgets for content presentation provided by the subset of publishing users, and determines changes in revenue to the online system from the increased budgets.
Abstract:
An online system receives multiple candidate components for including in content items to be presented to online system users. Upon identifying an opportunity to present content to a subject user of the online system, the online system dynamically generates an optimal content item for presentation to the subject user that includes one or more candidate components. Candidate components included in the optimal content item are selected by predicting an affinity of the subject user for each candidate component. The affinity of the subject user for a candidate component may be predicted using a machine-learned model that is trained using historical performance information about content items including the candidate component that were presented to viewing users of the online system having at least a threshold measure of similarity to the subject user. Components of content items used to train the model may be selected using a heuristic (e.g., Thompson sampling).
Abstract:
A social networking system provides content items to a user via a feed that may include one or more sponsored content items. Multiple sponsored content items may be included in a set that is presented in the feed via a scrollable content unit that presents a sponsored content item from the set and presents additional sponsored content items from the set when user interaction is received. To place sponsored content items in the feed, the social networking system scores a set of sponsored content items based on prior user interactions with content presented via scrollable content units and a bid amount of a sponsored content item in the set. The set of sponsored content items is ranked among other sponsored content items based on its score. If the set of sponsored content items is selected for inclusion in the feed, the social networking system orders the sponsored content items in the set for presentation via the scrollable content unit.
Abstract:
A social networking system presents a content feed including organic content items and sponsored content items to a user. To maintain user interaction with the content feed, the social networking system determines probabilities of the user performing various types of interactions with a sponsored content item and accounts for the determined probabilities when selecting content items for presentation via the content feed. For example, the social networking system generates a value for the sponsored content item based on the determined probabilities and determines a score for the sponsored content item based on the value and a bid amount associated with the sponsored content item. When selecting content for the content feed, the social networking system evaluates the sponsored content item based on its associated score. Prior interactions between the user and previously presented content may be used when determining the score for the sponsored content item.
Abstract:
An online system determines an estimated conversion rate for sponsored content items placed on content publishers and on the online system. The estimated conversion rate can be determined by a machine learning model trained using data describing content campaigns, content publishers, and online system users. This data is collected by the online system from content publishers and/or content campaigns that report conversion rates to the online system. By determining a ratio of estimated conversion rates with third party content on the content publisher against those on the online system, the online system can determine a publisher quality score for that content publisher. The online system uses the publisher quality score to normalize third party value contributions toward placing sponsored content on content publishers and the online system. Thus, disparities in the intrinsic value across publishers are diminished as third party value contributions are normalized based on the publisher conversion rates.
Abstract:
An advertising campaign received by an online system has a specified budget, a specified duration, and includes multiple advertisement requests that each include advertisements for presentation to users of the online system. An ad request included in the advertising campaign is associated with a frequency limit specifying a maximum number of times an advertisement from the ad request is shown to a user during the specified duration. When selecting advertisements for presentation to a user, the online system determines an adjustment value for the ad request's bid amount based on a number of times the advertisement from the ad request has been presented to the user, an amount of the duration that has lapsed, and the frequency limit associated with the ad request. The online system modifies the ad request's bid amount by the adjustment value and uses the modified bid amount when selecting advertisements for presentation to the user.
Abstract:
An online system receives content items from publishing users for presentation to other users. When selecting content for presentation to users, the online system accounts for amounts of compensation from publishing users when presenting content items. To prevent publishing users from exploiting content selection by the online system to obtain disproportionate presentation of their content items relative to other publishing users, the online system generates an estimated amount of revenue from various publishing users from presenting their content items. The online system compares an amount of compensation received from a publishing user to the estimated amount of revenue from the publishing user, and generates clusters of content items from the publishing user for review if the amount of compensation is at least a threshold amount less than the estimated amount of revenue.