Abstract: In some examples, an environment evaluation system accesses interaction data recording interactions by users with an online platform hosted by a host system and computes, based on the interaction data, interface experience metrics. The interface experience metrics includes an individual experience metric for each user and a transition experience metric for each transition in the interactions by the users with the online platform. The environment evaluation system identifies a user with the individual experience metric below a pre-determined threshold, identifies a transition performed by the user that has a transition experience metric below a second threshold, and analyzes the transition to determine users who have performed the transition. The environment evaluation system updates the host system with the individual experience metrics and the transition metrics, based on which the host system can perform modifications of interface elements of the online platform to improve the experience.

Abstract: In implementations of systems for predicting a terminal event, a computing device implements a termination system to receive input data defining a period of time and a maximum event threshold. This system uses a classification model to generate event scores for a plurality of entity devices. Each of the event scores indicates a probability of an event occurrence for a corresponding entity device within a period of time. The plurality of entity devices are segmented into a first segment and a second segment based on an event score threshold. Entity devices included in the first segment have event scores greater than the event score threshold and entity devices included in the second segment have event scores below the event score threshold. The termination system generates an indication of a probability that a number of event occurrences for the entity devices included in the second segment exceeds the maximum even threshold within the period of time.

Abstract: Methods and systems disclosed herein relate generally to systems and methods for using a machine-learning model to predict user-engagement levels of users in response to presentation of future interactive content. A content provider system accesses a machine-learning model, which was trained using a training dataset including previous user-device actions performed by a plurality of users in response to previous interactive content. The content provider system receives user-activity data of a particular user and applies the machine-learning model to the user-activity data, in which the user-activity data includes user-device actions performed by the particular user in response to interactive content. The machine-learning model generates an output including a categorical value that represents a predicted user-engagement level of the particular user in response to a presentation of the future interactive content.

Abstract: Certain embodiments involve extracting seasonal, level, and spike components from a time series of metrics data, which describe interactions with an online service over a time period. For example, an analytical system decomposes the time series into latent components that include a seasonal component series, a level component series, a spike component series, and an error component series. The decomposition involves configuring an optimization algorithm with a constraint indicating that the time series is a sum of these latent components. The decomposition also involves executing the optimization algorithm to minimize an objective function subject to the constraint and identifying, from the executed optimization algorithm, the seasonal component series, the level component series, the spike component series, and the error component series that minimize the objective function. The analytical system outputs at least some latent components for anomaly-detection or data-forecasting.

Abstract: A method, apparatus, and non-transitory computer readable medium for data analytics are described. Embodiments of the method, apparatus, and non-transitory computer readable medium include monitoring online activity corresponding to a plurality of users; receiving aggregate marketing data for a marketing activity; identifying online activity data for a time period corresponding to the marketing activity based on the monitoring; generating a regression model based on the aggregate marketing data and the online activity data using Bayesian regression, wherein the regression model represents a relationship between the marketing activity and the online activity, comprises a time effect coefficient, and is based on a prior distribution of the time effect coefficient that decays to zero as time increases; and estimating a treatment effect for the marketing activity on the online activity based on the regression model, wherein the treatment effect comprises a rate of effect decay.

Abstract: A method and system for outage forecasting are described. One or more aspects of the method and system include receiving, by a machine learning model, time series data for a service metric of a computer network; generating, by the machine learning model, probability distribution information for the service metric based on the time series data, wherein the probability distribution information is generated using a machine learning model that is trained using a distribution loss and a classification loss; and generating, by a forecasting component, outage forecasting information for the computer network based on the probability distribution information.

Abstract: In implementations of systems for cloud-based resource allocation using meters, a computing device implements a resource system to receive resource data describing an amount of cloud-based resources reserved for consumption by client devices during a period of time and a total amount of cloud-based resources consumed by the client devices during the period of time. The resource system determines a consumption distribution using each meter included in a set of meters. Each of the consumption distributions allocates a portion of the total amount of the cloud-based resources consumed to each client device of the client devices. A particular meter used to determine a particular consumption distribution is selected based on a Kendall Tau coefficient of the particular consumption distribution. An amount of cloud-based resources to allocate for a future period of time is estimated using the particular meter and an approximate Shapley value.

Abstract: A system implements a key value memory network including a key matrix with key vectors learned from training static feature data and time-series feature data, a value matrix with value vectors representing time-series trends, and an input layer to receive, for a target entity, input data comprising a concatenation of static feature data of the target entity, time-specific feature data, and time-series feature data for the target entity. The key value memory network also includes an entity-embedding layer to generate an input vector from the input data, a key-addressing layer to generate a weight vector indicating similarities between the key vectors and the input vector, a value-reading layer to compute a context vector from the weight and value vectors, and an output layer to generate predicted time-series data for a target metric of the target entity by applying a continuous activation function to the context vector and the input vector.

Abstract: A system implements a key value memory network including a key matrix with key vectors learned from training static feature data and time-series feature data, a value matrix with value vectors representing time-series trends, and an input layer to receive, for a target entity, input data comprising a concatenation of static feature data of the target entity, time-specific feature data, and time-series feature data for the target entity. The key value memory network also includes an entity-embedding layer to generate an input vector from the input data, a key-addressing layer to generate a weight vector indicating similarities between the key vectors and the input vector, a value-reading layer to compute a context vector from the weight and value vectors, and an output layer to generate predicted time-series data for a target metric of the target entity by applying a continuous activation function to the context vector and the input vector.

Abstract: Techniques for exchanging data segments between data aggregators and data consumers. In an embodiment, a value of an arbitrary data segment selected by a data consumer is computed. In particular, an individual user value is calculated for each user represented in the data segment, wherein the individual user value is a weighted sum (or other function) of the one or more features of the data segment attributable to that user, plus an additive gaussian noise. The overall value of the data segment is the sum of the individual user values. An offer price for the data segment can then be calculated using the overall value. Once a request is received from the consumer to purchase the data segment at the offer price, the data segment can be exchanged between the aggregator and consumer. Thus, a data marketplace or platform for the exchange of data segments is enabled.

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: This disclosure involves using key-value memory networks to predict time-series data. For instance, a computing system retrieves, for a target entity, static feature data and target time-series feature data. The computing system can normalize the target time-series feature data based on a normalization scale. The computing system also generates input data by, for example, concatenating the static feature data, the normalized time-series feature data, and time-specific feature data. The computing system generates predicted time-series data for the target metric of the target entity by applying a key-value memory network to the input data. The key-value memory network can include a key matrix learned from training static feature data and training time-series feature data, a value matrix representing time-series trends, and an output layer with a continuous activation function for generating predicted time-series data.

Abstract: The present disclosure describes systems, methods, and non-transitory computer readable media for generating and providing a causal-graph interface that visually depicts causal relationships among dimensions and represents uncertainty metrics for such relationships as part of a streamlined visualization of a causal graph. The disclosed systems can determine causality among dimensions of multidimensional data and determine uncertainty metrics associated with individual causal relationships. Additionally, the disclosed system can generate a visual representation of a causal graph with nodes arranged in stratified layers and can connect the layered nodes with uncertainty-aware-causal edges to represent both the causality between the dimensions and the uncertainty metrics.

Abstract: The present disclosure describes systems, methods, and non-transitory computer readable media for generating and providing a causal-graph interface that visually depicts causal relationships among dimensions and represents uncertainty metrics for such relationships as part of a streamlined visualization of a causal graph. The disclosed systems can determine causality among dimensions of multidimensional data and determine uncertainty metrics associated with individual causal relationships. Additionally, the disclosed system can generate a visual representation of a causal graph with nodes arranged in stratified layers and can connect the layered nodes with uncertainty-aware-causal edges to represent both the causality between the dimensions and the uncertainty metrics.

Abstract: Systems and methods for data analytics are described. The systems and methods include receiving attribute data for at least one user, identifying a plurality of precursor events causally related to an observable target interaction with the at least one user, wherein at least one of the precursor events comprises a marketing event, predicting a probability for each of the precursor events based on the attribute data using a neural network trained with a first loss function comparing individual level training data for the observable target interaction, and performing the marketing event directed to the at least one user based at least in part on the predicted probabilities.

Abstract: A method, apparatus, and non-transitory computer readable medium for data analytics are described. Embodiments of the method, apparatus, and non-transitory computer readable medium include monitoring online activity corresponding to a plurality of users; receiving aggregate marketing data for a marketing activity; identifying online activity data for a time period corresponding to the marketing activity based on the monitoring; generating a regression model based on the aggregate marketing data and the online activity data using Bayesian regression, wherein the regression model represents a relationship between the marketing activity and the online activity, comprises a time effect coefficient, and is based on a prior distribution of the time effect coefficient that decays to zero as time increases; and estimating a treatment effect for the marketing activity on the online activity based on the regression model, wherein the treatment effect comprises a rate of effect decay.

Abstract: Techniques for exchanging data segments between data aggregators and data consumers. In an embodiment, a value of an arbitrary data segment selected by a data consumer is computed. In particular, an individual user value is calculated for each user represented in the data segment, wherein the individual user value is a weighted sum (or other function) of the one or more features of the data segment attributable to that user, plus an additive gaussian noise. The overall value of the data segment is the sum of the individual user values. An offer price for the data segment can then be calculated using the overall value. Once a request is received from the consumer to purchase the data segment at the offer price, the data segment can be exchanged between the aggregator and consumer. Thus, a data marketplace or platform for the exchange of data segments is enabled.

Abstract: Techniques of forecasting web metrics involve generating, prior to the end of a period of time, a probability of a metric taking on an anomalous value, e.g., a value indicative of an anomaly with respect to web traffic, at the end of the period based on previous values of the metric. Such a probability is based on a distribution of predicted values of the metric at some previous period of time. For example, a web server may use actual values of the number of bounces collected at hourly intervals in the middle of a day to predict a number of bounces at the end of the current day. Further, the web server may also compute a confidence interval to determine whether a predicted end-of-day number of bounces may be considered anomalous. The width of the confidence interval indicates the probability that a predicted end-of-day number of bounces has an anomalous value.

Abstract: This disclosure involves allocating content-delivery resources to electronic content-delivery channels based on attribution models accuracy. For instance, a simulation is executed that involves simulating user exposures, times between user exposures, and user responses. The simulation is performed based on parameters associated with simulating user exposures to electronic content-delivery channels and user responses to the user exposures. An accuracy of a channel attribution model when estimating an attribution of an electronic content-delivery channel to a user response is evaluated based on the simulation. A channel attribution model is selected based on the evaluation. An attribution of the electronic content-delivery channel is determined by applying the selected channel attribution model to actual user exposures and actual user responses.

Abstract: This disclosure involves using key-value memory networks to predict time-series data. For instance, a computing system retrieves, for a target entity, static feature data and target time-series feature data. The computing system can normalize the target time-series feature data based on a normalization scale. The computing system also generates input data by, for example, concatenating the static feature data, the normalized time-series feature data, and time-specific feature data. The computing system generates predicted time-series data for the target metric of the target entity by applying a key-value memory network to the input data. The key-value memory network can include a key matrix learned from training static feature data and training time-series feature data, a value matrix representing time-series trends, and an output layer with a continuous activation function for generating predicted time-series data.