Abstract: An online system calculates bids for content items to display to users based on the value of a product described in the content item and the likelihood of a viewing user purchasing the product. The online system identifies an impression opportunity for an ad request and computes an expected value of the conversion and a likelihood of the conversion. The online system computes a bid amount based on the expected conversion value and the likelihood of the conversion. Bids based on the value of the conversion allow a third party system offering the product to optimize for the value of each conversion instead of the conversion rate.

Abstract: An online system calculates bids for content items to display to users based on the value of a product described in the content item and the likelihood of a viewing user purchasing the product. The online system identifies an impression opportunity for an ad request and computes an expected value of the conversion and a likelihood of the conversion. The online system computes a bid amount based on the expected conversion value and the likelihood of the conversion. Bids based on the value of the conversion allow a third party system offering the product to optimize for the value of each conversion instead of the conversion rate.

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 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 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: An online system selects content items for a user to increase probabilities of the user remembering the content items after presentation. The online system generates one or more models based on information describing amounts of time users have viewed previously presented content items. Hence, a model associated with a user predicts an amount of time the user will view a content item. When selecting content items for the user, the online system selects one or more content items that the user is predicted to view for an amount of time within a specific range, which may be based on amounts of times other users have viewed the content item or content items similar to the content item. For example, the online system increases a probability of selecting a content item the user is predicted to view for an amount of time within the specific range.

Abstract: An advertiser specifies an advertising campaign along with a budget, a duration and one or more objectives associated with the advertising campaign. An online system presenting advertisements from the advertising campaign determines an interval representing a portion of the duration, an interval budget, and one or more interval objectives. Historical information describing selection and presentation of advertisements by the online system is retrieved, and used to determine one or more filters limiting the number of opportunities to present an advertisement in which advertisements from the advertising campaign are included. The scaling filters may be based on the one or more interval objectives, bid amounts determined for advertisements in the advertising campaign, and bid amounts associated with advertisements previously selected for presentation form the historical data.

Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer readable media that use multi-point optimization for delivery of digital content by way of a digital content distribution platform. In particular, one or more embodiments described herein receive a content item from a content provider to be displayed to users of the platform. The embodiments optimize delivery to obtain a first target event and determine metrics that delivery of the content item is expected to satisfy. If the actual metrics of delivery fail to satisfy the expected metrics, delivery is re-optimized to obtain either the first target event or a second target event.

Abstract: An online system selects advertisements for inclusion in a scrollable advertisement unit that includes a display area and multiple advertisements, each associated with a position in the scrollable advertisement unit. Positions in the scrollable advertisement unit are ranked based on a measure of predicted performance of an advertisement in each position. Advertisements are ranked based on a probability of being viewed by a user if associated with a particular position in the scrollable advertisement unit and, optionally, on a probability of presenting an advertisement based on characteristics of the advertisement. The position ranking and the advertisement ranking are used to associate advertisements with positions. For example, an advertisement is associated with a position having a location in the position ranking corresponding to the advertisement's position in the advertisement ranking.

Abstract: An online system determines the pricing of an advertisement included in a scrollable advertisement unit including multiple advertisements and a display area presenting one or more advertisements. When an advertisement is included in a scrollable advertisement unit, a price for presentation of the advertisement is determined based on bid amounts of advertisements evaluated for inclusion in the scrollable advertisement unit and a scaling factor. The scaling factor is based on the probability of the advertisement being presented to a user based on the position in the scrollable advertisement unit associated with the advertisement. Advertisement characteristics affecting the likelihood of an advertisement being presented to a user may also be considered in the scaling factor.

Abstract: An advertiser specifies an advertising campaign along with a budget, a duration and one or more objectives associated with the advertising campaign. An online system presenting advertisements from the advertising campaign determines an interval representing a portion of the duration, an interval budget, and one or more interval objectives. Historical information describing selection and presentation of advertisements by the online system is retrieved, and used to determine one or more filters limiting the number of opportunities to present an advertisement in which advertisements from the advertising campaign are included. The scaling filters may be based on the one or more interval objectives, bid amounts determined for advertisements in the advertising campaign, and bid amounts associated with advertisements previously selected for presentation form the historical data.

Abstract: A social networking system dynamically adjusts a number of advertisements presented to a user along with organic content items by modifying a minimum bid amount associated with advertisements eligible for presentation to the user. Increasing the minimum bid amount decreases the number of advertisements presented to the user while decreasing the minimum bid amount increases the number of advertisements presented to the user. An engagement score measuring the user's estimated interaction with a content feed including organic content items without advertisements and an engagement score measuring the user's estimated interaction with a content feed including organic content items and advertisements are determined. A target score is determined based on the engagement scores, and a difference between the target score and a threshold value is used to modify a minimum price of advertisements eligible for presentation to the user.

Abstract: An advertiser specifies an advertising campaign along with a budget, a duration and one or more objectives associated with the advertising campaign. An online system presenting advertisements from the advertising campaign determines an interval representing a portion of the duration, an interval budget, and one or more interval objectives. Historical information describing selection and presentation of advertisements by the online system is retrieved, and used to determine one or more filters limiting the number of opportunities to present an advertisement in which advertisements from the advertising campaign are included. The scaling filters may be based on the one or more interval objectives, bid amounts determined for advertisements in the advertising campaign, and bid amounts associated with advertisements previously selected for presentation form the historical data.

Abstract: For ad campaigns that have multiple advertisements, each associated with an ad creative, which are automatically selected, an online system may bias selection of advertisements away from underestimated advertisements and towards early-selected advertisements with positive user interactions. To increase the likelihood of various advertisements in an ad campaign being evaluated for presentation to users, the online system may: associate a relatively high performance score with each advertisement in an ad campaign, randomly select advertisements from an ad campaign, modify bid amounts associated with advertisements in the ad campaign, or allocate a portion of the ad campaign's budget for allocation across advertisements in the ad campaign. After presenting a threshold number of advertisements in an ad campaign or receiving an instruction from an advertiser, advertisements from the ad campaign may be selected using conventional methods.

Abstract: A method for enabling a content sponsor to design a cost-effective content campaign. The content sponsor initiates a content campaign. The content sponsor is presented with a set of proposed channels on which to display the content and either accepts the proposed channels or selects an alternative set of channels. If an alternative set of channels is chosen, the content sponsor is presented with an alert comprising data predicting the result of presenting the content on the alternative set of channels. If the content sponsor does not heed the alert, the content sponsor is presented with an option to implement a split test, which involves implementing multiple content campaigns using different sets of channels. Finally, a result report is presented to the content sponsor. In some embodiments, the result report comprises a counterfactual report comparing the results of two or more campaigns implemented using different sets of channels.

Abstract: An online system presents a scrollable advertisement unit including multiple advertisements to a user. The scrollable advertisement unit presents one or more advertisements in a display area, and allows a user to navigate through the advertisements in the scrollable advertisement unit to display different advertisements in the display area. One or more rules for crediting an impression to an advertisement in the display area are applied, and a tracking mechanism associated with the advertisement in the display area is loaded if at least one rule is specified. Loading the tracking mechanism identifies an impression of its associated advertisement.

Abstract: An online system calculates bids for content items to display to users based on the value of a product described in the content item and the likelihood of a viewing user purchasing the product. The online system identifies an impression opportunity for an ad request and computes an expected value of the conversion and a likelihood of the conversion. The online system computes a bid amount based on the expected conversion value and the likelihood of the conversion. Bids based on the value of the conversion allow a third party system offering the product to optimize for the value of each conversion instead of the conversion rate.

Abstract: An online system selects content items for a user to increase probabilities of the user remembering the content items after presentation. The online system generates one or more models based on information describing amounts of time users have viewed previously presented content items. Hence, a model associated with a user predicts an amount of time the user will view a content item. When selecting content items for the user, the online system selects one or more content items that the user is predicted to view for an amount of time within a specific range, which may be based on amounts of times other users have viewed the content item or content items similar to the content item. For example, the online system increases a probability of selecting a content item the user is predicted to view for an amount of time within the specific range.

Abstract: An online system selects content items for a user to increase probabilities of the user remembering the content items after presentation. The online system generates one or more models based on information describing amounts of time users have viewed previously presented content items. Hence, a model associated with a user predicts an amount of time the user will view a content item. When selecting content items for the user, the online system selects one or more content items that the user is predicted to view for an amount of time within a specific range, which may be based on amounts of times other users have viewed the content item or content items similar to the content item. For example, the online system increases a probability of selecting a content item the user is predicted to view for an amount of time within the specific range.

Abstract: An advertisement system measures an ad lift metric for advertisement campaigns, which indicates the increase in conversions that can be attributed to the advertisement campaign. As impression opportunities become available for users for the ad in the lift study, the advertisement system determines whether the user is in a test group or a control group. To limit bias in the lift study, rather than holding out ads from being provided to users after the ad has been selected for the user and right before the impression, the system holds out the ads at a higher level in the ad selection process. In this manner, not all test group users receive the advertisement. The system computes the lift metric as e.g., the incremental lift (difference between conversion rates in the test and control groups), and this is divided by conversion rate of an exposed target group minus the incremental lift.