Abstract:
An advertising (“ad”) system allows users to specify a budget for an advertisement (“ad”) campaign including ad requests (“ads”), and the ad system automatically determines bids on a per-impression basis to pace the ad spend according to the budget. The ad system computes a “regret” metric for the ad campaign, which is the total amount over the course of the ad campaign that the advertiser had to pay for presenting an ad above the ideal bid (e.g., the bid that would have captured at least a threshold number of the lowest priced impressions during the ad campaign's life while spending the budget). The ad system may use the regret metric to indicate the performance of the ad campaign, e.g., as feedback for the advertiser.
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:
An advertising (“ad”) system allows users to specify a budget for an advertisement (“ad”) campaign including ad requests (“ads”), and the ad system automatically determines bids on a per-impression basis to pace the ad spend according to the budget. The ad system computes a “regret” metric for the ad campaign, which is the total amount over the course of the ad campaign that the advertiser had to pay for presenting an ad above the ideal bid (e.g., the bid that would have captured at least a threshold number of the lowest priced impressions during the ad campaign's life while spending the budget). The ad system may use the regret metric to indicate the performance of the ad campaign, e.g., as feedback for the advertiser.
Abstract:
A social networking system presents content items, such as news feed stories and advertisements, to a user of the social networking system via a news feed. The social networking system determines to again present a content item via the news feed or to present a previously presented content item in a different position of the news feed. The social networking system identifies additional content items to present to the user as well as content items previously presented to the user. The social networking system scores the additional content items and the previously presented content items, accounting for a cost of removing the previously presented content item from its original position for presentation in the alternative position. Based on the score the social networking system ranks the content items selects, based on the rank, content items to present to the user.
Abstract:
A system predicts user intent to take an action and delivers content items to the user that match that intent. A plurality of features or attributes for each tracking pixel in a set of tracking pixels can be acquired based on content items and landing pages associated with each tracking pixel. For example, features for a tracking pixel can be determined based on information associated with a content item that enabled a user to access a landing page from which the tracking pixel was fired or triggered. In this example, features for the tracking pixel can also be determined based on information associated with the landing page. The features for the tracking pixels can be utilized to train a machine learning model. The machine learning model can be trained to predict whether or not a particular user intends to produce a conversion (e.g., make a purchase).
Abstract:
An online system selecting content items for presentation to its users accounts for likelihoods of users performing actions associated with content items when selecting content items. The online system maintains models determining likelihoods of users performing various actions. If a content item is associated with an action that infrequently occurs, information for determining the model for the action is limited, so the online system increases a bid amount associated with the content item during a time interval to an amount based on a likelihood of the user performing a more frequently occurring alternative action and an average bid amount for the alternative action from content items previously presented to users. The online system also determines an amount based on the model for the action and the bid amount for during the time interval and stops increasing the bid amount when the rate of change has less than a threshold magnitude.
Abstract:
An online system receives a set of parameters for an advertising campaign and modifies one or more of the parameters to determine how the modified parameters affect presentation of advertisements from the advertising campaign. For example, the online system increases bid amounts or a budget for the advertising campaign for a number of impression opportunities for advertisements form the advertising system. The online system subsidizes the increase so a user providing the advertisement request is not charged extra for the increased bid amounts or budget. To offset potential benefits to the advertising campaign from the modified parameters, the online system withholds the advertising campaign from being considered for a certain number of impression opportunities for which the advertising campaign would otherwise be eligible.
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 applies advertising policies regulating presentation of sponsored content to its users. For example, advertising policies may prevent the presentation of advertisements in certain positions content feeds. The online system may relax an advertising policy for an advertisement meeting certain criteria, such as a likelihood of a user interacting with the advertisement or a predicted value of presenting the advertisement. If the online system relaxes an advertising policy for an advertisement, the online system computes a penalty incurred by the advertisement for violating the advertising policy. The online system computes a value for presenting a candidate feed presenting the advertisement in a position violating the advertising policy and a value for an alternative feed presenting the advertisement in a position complying with the advertising policy. The online system selects the candidate feed or the alternative feed for presentation to the user by comparing the values.
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.