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: In some embodiments, a knowledge graph generation system extracts noun-phrases from sentences of a knowledge corpora and determines the relations between the noun-phrases based on a relation classifier that is configured to predict a relation between a pair of entities without restricting the entities to a set of named entities. The knowledge graph generation system further generates a sub-graph for each of the sentences based on the noun-phrases and the determined relations. Nodes or entities of the sub-graph represent the non-phrases in the sentence and edges represent the relations between the noun-phrases connected by the respective edges. The knowledge graph generation system merges the sub-graphs to generate the knowledge graph for the knowledge corpora.

Abstract: Systems and methods for data analytics are described. One or more embodiments of the present disclosure receive target time series data and candidate time series data, where the candidate time series data includes data corresponding to each of a plurality of candidate metrics, train a prediction network to predict the target time series data based on the candidate time series data by setting temporal attention weights corresponding to a plurality of rolling time windows and by setting candidate attention weights corresponding to the plurality of candidate metrics, identify a leading indicator metric for the target time series data from the plurality of candidate metrics based on the temporal attention weights and the candidate attention weights, and signal the leading indicator metric for the target time series data.

Abstract: A method includes identifying interaction data associated with user interactions with a user interface of an interactive computing environment. The method also includes computing goal clusters of the interaction data based on sequences of the user interactions and performing inverse reinforcement learning on the goal clusters to return rewards and policies. Further, the method includes computing likelihood values of additional sequences of user interactions falling within the goal clusters based on the policies corresponding to each of the goal clusters and assigning the additional sequences to the goal clusters with greatest likelihood values. Furthermore, the method includes computing interface experience metrics of the additional sequences using the rewards and the policies corresponding to the goal clusters of the additional sequences and transmitting the interface experience metrics to the online platform.

Abstract: A method for predicting user purchase by a user of a first site includes: selecting a distribution representing a probability distribution (PD) of inter-purchase-times (IPTs) across the first site and a second other site for each user, assigning each purchase of each user to one of the first site and the second site according to a Stochastic model, combining the selected PD with the Stochastic model to generate a PD of IPTs for only the first online site, estimating parameters of the probability distribution of IPTs for the first site by applying a Statistical modeling approach to features of each user, applying a sequence of observed IPTs of a given user for the first site and the parameters of the given user to the selected distribution to generate a probability, and determining whether the next purchase occurs on the second site based on the probability.

Abstract: A method includes accessing a subject entity and a subject relation of a focal platform and accessing a knowledge graph representative of control performance data. Further, the method includes computing a set of ranked target entities that cause the subject entity based on the subject relation or are an effect of the subject entity based on the subject relation. Computing the set of ranked target entities is performed using relational hops from the subject entity within the knowledge graph performed using the subject relation and reward functions. The method also includes transmitting the set of ranked target entities to the focal platform. The set of ranked target entities is usable for modifying a user interface of an interactive computing environment provided by the focal platform.

Abstract: In some embodiments, a knowledge graph generation system extracts noun-phrases from sentences of a knowledge corpora and determines the relations between the noun-phrases based on a relation classifier that is configured to predict a relation between a pair of entities without restricting the entities to a set of named entities. The knowledge graph generation system further generates a sub-graph for each of the sentences based on the noun-phrases and the determined relations. Nodes or entities of the sub-graph represent the non-phrases in the sentence and edges represent the relations between the noun-phrases connected by the respective edges. The knowledge graph generation system merges the sub-graphs to generate the knowledge graph for the knowledge corpora.

Abstract: A method for predicting user purchase by a user of a first site includes: selecting a distribution representing a probability distribution (PD) of inter-purchase-times (IPTs) across the first site and a second other site for each user, assigning each purchase of each user to one of the first site and the second site according to a Stochastic model, combining the selected PD with the Stochastic model to generate a PD of IPTs for only the first online site, estimating parameters of the probability distribution of IPTs for the first site by applying a Statistical modeling approach to features of each user, applying a sequence of observed IPTs of a given user for the first site and the parameters of the given user to the selected distribution to generate a probability, and determining whether the next purchase occurs on the second site based on the probability.

Abstract: In some embodiments, a knowledge graph generation system extracts noun-phrases from sentences of a knowledge corpora and determines the relations between the noun-phrases based on a relation classifier that is configured to predict a relation between a pair of entities without restricting the entities to a set of named entities. The knowledge graph generation system further generates a sub-graph for each of the sentences based on the noun-phrases and the determined relations. Nodes or entities of the sub-graph represent the non-phrases in the sentence and edges represent the relations between the noun-phrases connected by the respective edges. The knowledge graph generation system merges the sub-graphs to generate the knowledge graph for the knowledge corpora.

Abstract: Systems and methods for data analytics are described. One or more embodiments of the present disclosure receive target time series data and candidate time series data, where the candidate time series data includes data corresponding to each of a plurality of candidate metrics, train a prediction network to predict the target time series data based on the candidate time series data by setting temporal attention weights corresponding to a plurality of rolling time windows and by setting candidate attention weights corresponding to the plurality of candidate metrics, identify a leading indicator metric for the target time series data from the plurality of candidate metrics based on the temporal attention weights and the candidate attention weights, and signal the leading indicator metric for the target time series data.

Abstract: A method includes identifying interaction data associated with user interactions with a user interface of an interactive computing environment. The method also includes computing goal clusters of the interaction data based on sequences of the user interactions and performing inverse reinforcement learning on the goal clusters to return rewards and policies. Further, the method includes computing likelihood values of additional sequences of user interactions falling within the goal clusters based on the policies corresponding to each of the goal clusters and assigning the additional sequences to the goal clusters with greatest likelihood values. Furthermore, the method includes computing interface experience metrics of the additional sequences using the rewards and the policies corresponding to the goal clusters of the additional sequences and transmitting the interface experience metrics to the online platform.

Abstract: The present disclosure describes systems, methods, and non-transitory computer readable media for generating lookalike segments corresponding to a target segment using decision trees and providing a graphical user interface comprising nodes representing such lookalike segments. Upon receiving an indication of a target segment, for instance, the disclosed systems can generate a lookalike segment from a set of users by partitioning the set of users according to one or more dimensions based on probabilities of subsets of users matching the target segment. By partitioning subsets of users within a node tree, the disclosed systems can identify different subsets of users partitioned according to different dimensions from the set of users. The disclosed systems can further provide a node tree interface comprising a node for the set of users and nodes for subsets of users within one or more lookalike segments.

Abstract: In some embodiments, interaction data associated with user interactions with a user interface of an interactive computing environment is identified, and goal clusters of the interaction data are computed based on sequences of the user interactions and performing inverse reinforcement learning on the goal clusters to return rewards and policies. Further, likelihood values of additional sequences of user interactions falling within the goal clusters are computed based on the policies corresponding to each of the goal clusters and assigning the additional sequences to the goal clusters with greatest likelihood values. Computing interface experience metrics of the additional sequences are computed using the rewards and the policies corresponding to the goal clusters of the additional sequences and transmitting the interface experience metrics to the online platform. The interface experience metrics are usable for changing arrangements of interface elements to improve the interface experience metrics.

Abstract: In some embodiments, a knowledge graph generation system extracts noun-phrases from sentences of a knowledge corpora and determines the relations between the noun-phrases based on a relation classifier that is configured to predict a relation between a pair of entities without restricting the entities to a set of named entities. The knowledge graph generation system further generates a sub-graph for each of the sentences based on the noun-phrases and the determined relations. Nodes or entities of the sub-graph represent the non-phrases in the sentence and edges represent the relations between the noun-phrases connected by the respective edges. The knowledge graph generation system merges the sub-graphs to generate the knowledge graph for the knowledge corpora.

Abstract: In some embodiments, interaction data associated with user interactions with a user interface of an interactive computing environment is identified, and goal clusters of the interaction data are computed based on sequences of the user interactions and performing inverse reinforcement learning on the goal clusters to return rewards and policies. Further, likelihood values of additional sequences of user interactions falling within the goal clusters are computed based on the policies corresponding to each of the goal clusters and assigning the additional sequences to the goal clusters with greatest likelihood values. Computing interface experience metrics of the additional sequences are computed using the rewards and the policies corresponding to the goal clusters of the additional sequences and transmitting the interface experience metrics to the online platform. The interface experience metrics are usable for changing arrangements of interface elements to improve the interface experience metrics.

Abstract: A method includes accessing a subject entity and a subject relation of a focal platform and accessing a knowledge graph representative of control performance data. Further, the method includes computing a set of ranked target entities that cause the subject entity based on the subject relation or are an effect of the subject entity based on the subject relation. Computing the set of ranked target entities is performed using relational hops from the subject entity within the knowledge graph performed using the subject relation and reward functions. The method also includes transmitting the set of ranked target entities to the focal platform. The set of ranked target entities is usable for modifying a user interface of an interactive computing environment provided by the focal platform.

Abstract: Systems and methods provide for generating predictive models that are useful in predicting next-user-actions. User-specific navigation sequences are obtained, the navigation sequences representing temporally-related series of actions performed by users during navigation sessions. To each navigation sequence, a Recurrent Neural Network (RNN) is applied to encode the navigation sequences into user embeddings that reflect time-based, sequential navigation patterns for the user. Once a set of navigation sequences is encoded to a set of user embeddings, a variety of classifiers (prediction models) may be applied to the user embeddings to predict what a probable next-user-action may be and/or the likelihood that the next-user-action will be a desired target action.

Abstract: Systems and methods provide for generating predictive models that are useful in predicting next-user-actions. User-specific navigation sequences are obtained, the navigation sequences representing temporally-related series of actions performed by users during navigation sessions. To each navigation sequence, a Recurrent Neural Network (RNN) is applied to encode the navigation sequences into user embeddings that reflect time-based, sequential navigation patterns for the user. Once a set of navigation sequences is encoded to a set of user embeddings, a variety of classifiers (prediction models) may be applied to the user embeddings to predict what a probable next-user-action may be and/or the likelihood that the next-user-action will be a desired target action.

Abstract: Systems and methods provide for generating predictive models that are useful in predicting next-user-actions. User-specific navigation sequences are obtained, the navigation sequences representing temporally-related series of actions performed by users during navigation sessions. To each navigation sequence, a Recurrent Neural Network (RNN) is applied to encode the navigation sequences into user embeddings that reflect time-based, sequential navigation patterns for the user. Once a set of navigation sequences is encoded to a set of user embeddings, a variety of classifiers (prediction models) may be applied to the user embeddings to predict what a probable next-user-action may be and/or the likelihood that the next-user-action will be a desired target action.

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for determining and applying digital content transmission times using machine-learning. For example, in one or more embodiments, the disclosed system trains a recurrent neural network based on past electronic messages for a user that have been partitioned into a plurality of time bins. Additionally, in one or more embodiments, the system utilizes the trained recurrent neural network to generate predictions of engagement metrics (e.g., a hazard metric based on survival analysis or interaction probability metric) for sending a new electronic message within the plurality of time bins. The system then executes the digital content campaign by selecting a time bin based on the predicted engagement metrics and then sending the new electronic message at a send time corresponding to the selected time bin.