Abstract: Techniques and systems are described in which a document management system is configured to update content of digital documents through use of static and transient tags. A transient tag, for instance, may be associated with portions of the digital document that may be changed and a static tag with portions of the digital document that are not to be changed. An update to the digital document is then triggered by a document management system based on a triggering change made to an initial document portion of the digital document having a transient tag, and is not based on changes made to portions having a static tag or are untagged.

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for detecting robotic activity while monitoring Internet traffic across a plurality of domains. For example, the disclosed system identifies network session data for each domain of a plurality of domains, the network session data including network sessions comprising features that indicate human activity. In one or more embodiments, the disclosed system generates a classifier to output a probability that a network session at a domain includes human activity. In one or more embodiments, the disclosed system also generates a classifier to output a probability that a network session includes good robotic activity. Additionally, the disclosed system generates a domain-agnostic machine-learning model by combining models from a plurality of domains with network sessions including human activity.

Abstract: The present disclosure includes systems, methods, and non-transitory computer readable media that utilize a genetic framework to generate enhanced digital layouts from digital content fragments. In particular, in one or more embodiments, the disclosed systems iteratively generate a layout chromosome of digital content fragments, determine a fitness level of the layout chromosome, and mutate the layout chromosome until converging to an improved fitness level. The disclosed systems can efficiently utilize computing resources to generate a digital layout from a layout chromosome that is optimized to specified platforms, distribution audiences, and target optimization goals.

Abstract: A session identification system classifies network sessions with a network application as either human-generated or generated by a non-human, such as by a bot. In an embodiment, the session identification system receives a set of unlabeled network sessions, and determines a label for a single class of the unlabeled network sessions. Based on the one-class labeling information, the session identification system determines multiple subsets of the unlabeled network sessions. Multiple classifiers included in the session identification system generate probabilities describing each of the unlabeled network sessions. The session identification system classifies each of the unlabeled network sessions based on a combination of the generated probabilities.

Abstract: The present disclosure is directed toward systems, methods, and computer readable media for training and utilizing an item-level importance sampling model to evaluate and execute digital content selection policies. For example, systems described herein include training and utilizing an item-level importance sampling model that accurately and efficiently predicts a performance value that indicates a probability that a target user will interact with ranked lists of digital content items provided in accordance with a target digital content selection policy. Specifically, systems described herein can perform an offline evaluation of a target policy in light of historical user interactions corresponding to a training digital content selection policy to determine item-level importance weights that account for differences in digital content item distributions between the training policy and the target policy.

Abstract: The present disclosure is directed toward systems, methods, and computer readable media for training and utilizing an item-level importance sampling model to evaluate and execute digital content selection policies. For example, systems described herein include training and utilizing an item-level importance sampling model that accurately and efficiently predicts a performance value that indicates a probability that a target user will interact with ranked lists of digital content items provided in accordance with a target digital content selection policy. Specifically, systems described herein can perform an offline evaluation of a target policy in light of historical user interactions corresponding to a training digital content selection policy to determine item-level importance weights that account for differences in digital content item distributions between the training policy and the target policy.

Abstract: A fact replacement and style consistency tool is described. Rather than rely heavily on human involvement to replace facts and maintain consistent styles across multiple digital documents, the described change management system identifies factual and stylistic inconsistencies between these documents, in part, using natural language processing techniques. Once these inconsistencies are identified, the change management system generates a user interface that includes indications of the inconsistencies and information describing them, e.g., an indication noting not only a type of inconsistency but also presenting a first portion and at least a second portion of the multiple documents that are factually inconsistent.

Abstract: The present disclosure includes systems, methods, and non-transitory computer readable media that utilize a genetic framework to generate enhanced digital layouts from digital content fragments. In particular, in one or more embodiments, the disclosed systems iteratively generate a layout chromosome of digital content fragments, determine a fitness level of the layout chromosome, and mutate the layout chromosome until converging to an improved fitness level. The disclosed systems can efficiently utilize computing resources to generate a digital layout from a layout chromosome that is optimized to specified platforms, distribution audiences, and target optimization goals.

Abstract: Techniques and systems are described in which a document management system is configured to update content of document portions of digital documents. In one example, an update to the digital document is initially triggered by a document management system by detecting a triggering change applied to an initial portion of the digital document. The document management system, in response to the triggering change, then determines whether trailing changes are to be made to other document portions, such as to other document portions in the same digital document or another digital document. To do so, triggering and trailing change representations are generated and compared to determine similarity of candidate document portions with an initial document portion.

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for detecting robotic activity while monitoring Internet traffic across a plurality of domains. For example, the disclosed system identifies network session data for each domain of a plurality of domains, the network session data including network sessions comprising features that indicate human activity. In one or more embodiments, the disclosed system generates a classifier to output a probability that a network session at a domain includes human activity. In one or more embodiments, the disclosed system also generates a classifier to output a probability that a network session includes good robotic activity. Additionally, the disclosed system generates a domain-agnostic machine-learning model by combining models from a plurality of domains with network sessions including human activity.

Abstract: The present disclosure is directed toward systems, methods, and computer readable media for training and utilizing an item-level importance sampling model to evaluate and execute digital content selection policies. For example, systems described herein include training and utilizing an item-level importance sampling model that accurately and efficiently predicts a performance value that indicates a probability that a target user will interact with ranked lists of digital content items provided in accordance with a target digital content selection policy. Specifically, systems described herein can perform an offline evaluation of a target policy in light of historical user interactions corresponding to a training digital content selection policy to determine item-level importance weights that account for differences in digital content item distributions between the training policy and the target policy.

Abstract: This disclosure involves predictive modeling with entity representations computed from neural network models simultaneously trained on multiple tasks. For example, a method includes a processing device performing operations including accessing input data for an entity and transforming the input data into a dense vector entity representation representing the entity. Transforming the input data includes applying, to the input data, a neural network including simultaneously trained propensity models. Each propensity model predicts a different task based on the input data. Transforming the input data also includes extracting the dense vector entity representation from a common layer of the neural network to which the propensity models are connected.

Abstract: Techniques and systems are described in which a document management system is configured to update content of document portions of digital documents. In one example, an update to the digital document is initially triggered by a document management system by detecting a triggering change applied to an initial portion of the digital document. The document management system, in response to the triggering change, then determines whether trailing changes are to be made to other document portions, such as to other document portions in the same digital document or another digital document. To do so, triggering and trailing change representations are generated and compared to determine similarity of candidate document portions with an initial document portion.

Abstract: Techniques and systems are described in which a document management system is configured to update content of digital documents through use of static and transient tags. A transient tag, for instance, may be associated with portions of the digital document that may be changed and a static tag with portions of the digital document that are not to be changed. An update to the digital document is then triggered by a document management system based on a triggering change made to an initial document portion of the digital document having a transient tag, and is not based on changes made to portions having a static tag or are untagged.

Abstract: The present disclosure relates applying a survival analysis to model when a particular recipient will view an electronic message. For example, one or more embodiments train a survivor function to model the time that will elapse, on a continuous scale, before a recipient will open an electronic message. For example, one or more embodiments involve accessing analytics training data and extracting a first set of features affecting the time that elapsed before past recipients opened an electronic message and a second set of features affecting whether the recipients opened the electronic message at all. The system then generates a mixture model modified survivor function and determines the effect of each feature set on its corresponding outcome to learn parameters for the mixture model modified survivor function.

Abstract: A session identification system classifies network sessions with a network application as either human-generated or generated by a non-human, such as by a bot. In an embodiment, the session identification system receives a set of unlabeled network sessions, and determines a label for a single class of the unlabeled network sessions. Based on the one-class labeling information, the session identification system determines multiple subsets of the unlabeled network sessions. Multiple classifiers included in the session identification system generate probabilities describing each of the unlabeled network sessions. The session identification system classifies each of the unlabeled network sessions based on a combination of the generated probabilities.

Abstract: Generating and providing a content feed to a user that surfaces information items that are determined to be interesting or relevant to the user including content that is determined to be “distant” to the user is provided. Explicit user actions are used to discover peers who are not colleagues of the user (e.g., peers with whom the user does not share a close organizational relationship, peers with whom the user does not regularly communicate, etc.), but who the user indicates an interest in via his/her actions. These peers are categorized as elevated peers of the user, and information items associated with and trending around the elevated peers are surfaced to the user in a content feed.

Abstract: Generating and providing a content feed to a user that surfaces information items that are determined to be interesting or relevant to the user including content that is determined to be “distant” to the user is provided. Explicit user actions are used to discover peers who are not colleagues of the user (e.g., peers with whom the user does not share a close organizational relationship, peers with whom the user does not regularly communicate, etc.), but who the user indicates an interest in via his/her actions. These peers are categorized as elevated peers of the user, and information items associated with and trending around the elevated peers are surfaced to the user in a content feed.

Abstract: Online learning of click-through rates on search result blocks from one or more federated sources may be provided. Click-through feedback for the search result blocks may be received from the one or more federated sources in response to a query. Weights may be assigned to each of the search result blocks based on the received click-through feedback. The search result blocks may then be ranked based on the assigned weights. Finally, a search results page may be generated for displaying the ranked search results blocks to a user.

Abstract: A transaction rate estimate system may retrieve an earlier transaction rate estimate along with a new set of transaction data. The new set of transaction data may include a new transaction count and a new time period over which the new transaction count occurred. The system may process the retrieved data by estimating a new transaction rate using a conjugate prior pair with the first transaction rate estimate, the new transaction count, and the new time period as inputs. The system may also store the new transaction rate for use in future estimates and present applications.