Abstract: Aspects of the disclosure relate to data loss prevention. A computing platform may detect input of a first target recipient domain into a first email message. The computing platform may identify, in real time and prior to sending the first email message, that the first target recipient domain is an unintended recipient domain instead of an intended recipient domain. The computing platform may identify, in real time and prior to sending the first email message, that the first email message violates one or more data loss prevention rules. Based on identifying the violation, the computing platform may send a notification that the first target recipient domain is flagged as an unintended recipient domain and one or more commands directing a user device of the message sender to display the notification.

Abstract: Aspects of the disclosure relate to data loss prevention. A computing platform may detect input of a first target recipient domain into a first email message. The computing platform may identify, in real time and prior to sending the first email message, that the first target recipient domain comprises an unintended recipient domain instead of an intended recipient domain. The computing platform may send, based on the identification of the unintended recipient domain and to a user device, a notification that the first target recipient domain is flagged as an unintended recipient domain and one or more commands directing the user device to display the notification.

Abstract: Aspects of the disclosure relate to data loss prevention. A computing platform may detect input of a first target recipient domain into a first email message. The computing platform may identify, in real time and prior to sending the first email message, that the first target recipient domain is an unintended recipient domain instead of an intended recipient domain. The computing platform may identify, in real time and prior to sending the first email message, that the first email message violates one or more data loss prevention rules. Based on identifying the violation, the computing platform may send a notification that the first target recipient domain is flagged as an unintended recipient domain and one or more commands directing a user device of the message sender to display the notification.

Abstract: An online system provides a feed of content including organic content items and sponsored content items that are positioned relative to each other to maximize user interaction with the feed of content. To reduce latency of providing the feed of content to a user without impairing positioning of organic content items and sponsored content items relative to each other, the online system generates the feed of content including organic content items and sends the feed of content to a client device while selecting sponsored content items for the feed of content. The online system transmits selected sponsored content items to the client device, which modifies the feed of content to include the sponsored content items and presents the modified feed of content.

Abstract: A system trains models to generate embeddings that represent likelihoods associated with features. For example, an embedding may be generated for users and pages such that a user's embedding represents how likely a user is to comment on a given page. Initially, memory space for storing each embedding may be overprovisioned. The system monitors the embeddings for a feature as they are generated and recalculated over time. If the system detects that a particular index value is never updated for embeddings of that feature, then the system may remove that value from the feature embeddings. This allows the array lengths of embeddings to be customized to the particular features they represent, saving memory space. The system may further use related information to identify pooling functions that are most effective for particular features, to identify similarities between entities, and to provide insight into how the feature data influences neural network layers.

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 advertising system provides advertisements to client devices. To select advertisements, the advertising system identifies previously selected advertisements to determine which presentations of the advertisement are still in-flight and have not yet resulted in conversion event. The advertising system predicts total advertising spend based on the in-flight advertisements, and adjusts a paced bid for the advertisement by determining whether the estimated total advertising spend, reflecting predicted in-flight advertisements, is above or below an expected spending to reach a budget for the advertising campaign, which may increase or decrease the paced bid.

Abstract: An online system provides a feed of content including organic content items and sponsored content items that are positioned relative to each other to maximize user interaction with the feed of content. To reduce latency of providing the feed of content to a user without impairing positioning of organic content items and sponsored content items relative to each other, the online system initially selects a subset of sponsored content items based on characteristics (e.g., bid amounts) of the sponsored content items. Subsequently, the online system applies one or more selection processes to organic content items and to sponsored content items of the subset that accounts for positioning of sponsored content items and organic content items relative to each other within the feed of content. Hence, the online system evaluates the subset of sponsored content items along with organic content items when ordering content within the feed.

Abstract: An online system provides a feed of content including organic content items and sponsored content items that are positioned relative to each other to maximize user interaction with the feed of content. To reduce latency of providing the feed of content to a user without impairing positioning of organic content items and sponsored content items relative to each other, the online system generates the feed of content including organic content items and sends the feed of content to a client device while selecting sponsored content items for the feed of content. The online system transmits selected sponsored content items to the client device, which modifies the feed of content to include the sponsored content items and presents the modified feed of content.

Abstract: An online system provides a feed of content including organic content items and sponsored content items that are positioned relative to each other to maximize user interaction with the feed of content. To reduce latency of providing the feed of content to a user without impairing positioning of organic content items and sponsored content items relative to each other, the online system initially selects a subset of sponsored content items based on characteristics (e.g., bid amounts) of the sponsored content items. Subsequently, the online system applies one or more selection processes to organic content items and to sponsored content items of the subset that accounts for positioning of sponsored content items and organic content items relative to each other within the feed of content. Hence, the online system evaluates the subset of sponsored content items along with organic content items when ordering content within the feed.

Abstract: Methods and systems are described herein for jointly training embeddings. The method involves identifying a first data set describing occurrences of a first event type and identifying a second data set describing occurrences of a second event type, in which the first data set and the second data set include a set of users in common. The method further involves jointly training a set of embeddings a joint set of users, involving training the set of users in common based on co-occurrences of events of the first event type first data set and co-occurrences of events of the second event type in the second data set. The method further involves training a computer model that predicts the likelihood of occurrence of a future event for a user with respect to a content item based on the embedding for the user in the jointly trained set of embeddings.

Abstract: An online system presents different content items to different sets of users to evaluate how changes to content or changes to the online system affect user interaction with the content items or presentation of the content items. However, if the online system receives compensation for presenting different content items, the online system may receive a disproportionate amount of compensation for presenting one of the content items that improves user interaction. To prevent such disproportionate allocation of compensation between presentation of different contents items, the online system allocates sets of users to whom different content items are eligible to be presented to maintain a specified budget for presenting the different content items. The online system also differently allocates users across sets to whom different content items are eligible for presentation to prevent biasing of users from presentation of other different content items to users done in parallel.

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 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 system provides advertisements to client devices. To select advertisements, the advertising system identifies previously selected advertisements to determine which presentations of the advertisement are still in-flight and have not yet resulted in conversion event. The advertising system predicts total advertising spend based on the in-flight advertisements, and adjusts a paced bid for the advertisement by determining whether the estimated total advertising spend, reflecting predicted in-flight advertisements, is above or below an expected spending to reach a budget for the advertising campaign, which may increase or decrease the paced bid.