Abstract: Techniques are provided for analyzing user actions that have occurred over a time period. The user actions can be, for example, with respect to the user's navigation of content or interaction with an application. Such user data is provided in an action string, which is converted into a highly searchable format. As such, the presence and frequency of particular user actions and patterns of user actions within an action string of a particular user, as well as among multiple action strings of multiple users, are determinable. Subsequences of one or more action strings are identified and both the number of action strings that include a particular subsequence and the frequency that a particular subsequence is present in a given action string are determinable. The conversion involves breaking that string into a sorted list of locations for the actions within that string. Queries can be readily applied against the sorted list.

Abstract: A method for recommending digital content includes: determining user preferences and a time horizon of a given user; determining a group for the given user based on the determined user preferences; determining a number of users of the determined group and a similarity of the users; applying information including the number of users, the similarity, and the time horizon to a model selection classifier to select one of a personalized model of the user and a group model of the determined group; and running the selected model to determine digital content to recommend.

Abstract: Methods, computer systems, computer-storage media, and graphical user interfaces are provided for facilitating generation and presentation of insights. In one implementation, a set of data is used to generate a data visualization. A candidate insight associated with the data visualization is generated, the candidate insight being generated in text form based on a text template and comprising a descriptive insight, a predictive insight, an investigative, or a prescriptive insight. A set of natural language insights is generated, via a machine learning model. The natural language insights represent the candidate insight in a text style that is different from the text template. A natural language insight having the text style corresponding with a desired text style is selected for presenting the candidate insight and, thereafter, the selected natural language insight and data visualization are providing for display via a graphical user interface.

Abstract: In implementations of systems for generating node embeddings for multiple roles, a computing device implements an embeddings system to cluster nodes of a graph into clusters. An initial role membership vector is computed for each of the nodes based on the clusters. The embeddings system generates a first set of role embeddings for a particular node of the nodes based on the initial role membership vector for the particular node and nodes connected to the particular node in the graph. The embeddings system determines an indication of at least one of a node classification or a link prediction for the graph based on the first set of role embeddings and a second set of role embeddings for an additional node of the nodes.

Abstract: Methods, computer systems, computer-storage media, and graphical user interfaces are provided for facilitating generation and presentation of insights. In one implementation, a set of data is used to generate a data visualization. A candidate insight associated with the data visualization is generated, the candidate insight being generated in text form based on a text template and comprising a descriptive insight, a predictive insight, an investigative, or a prescriptive insight. A set of natural language insights is generated, via a machine learning model. The natural language insights represent the candidate insight in a text style that is different from the text template. A natural language insight having the text style corresponding with a desired text style is selected for presenting the candidate insight and, thereafter, the selected natural language insight and data visualization are providing for display via a graphical user interface.

Abstract: In implementations of systems for generating sequential supporting answer reports, a computing device implements a report system to receive a user input defining a question with respect to a visual representation of analytics data rendered in a user interface. The report system determines a final answer to the question by processing a semantic representation of the question using a machine learning model. A sequence of reports is generated and the sequence defines an order of progression from a first supporting answer to the final answer. Each report of the sequence of reports includes a visual representation of a supporting answer to the question. The report system displays a dashboard in the user interface including a first report of the sequence of reports, the first report depicting a visual representation of the first supporting answer to the question.

Abstract: In some embodiments, techniques for identifying bot activity are provided. For example, a process may involve receiving a plurality of samples, wherein each sample is a record of click activity; classifying the plurality of samples among a first class and a second class, using a machine learning model trained by a training process, to produce a corresponding plurality of classification predictions; filtering click activity data, based on information from the plurality of classification predictions, to produce filtered click activity data; and causing a user interface of a computing environment to be modified based on information from the filtered click activity data. The training process includes training the machine learning model to classify samples among the first and second classes, using a training set of samples of the first class, a training set of samples of the second class, and values of a topological loss function calculated based on the training sets.

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: Methods, systems, and non-transitory computer readable media are disclosed for intelligently generating comprehensive and relevant visual data stories from tabular data. The disclosed system can create data stories from a given dataset guided by some user selections to set a theme for the story. In particular, the disclosed system evaluates facts of a dataset to identify significant facts. The disclosed system can order significant facts to create a backbone for the data story based on user selections by utilizing a beam search. Furthermore, the disclosed system can expand the backbone of the data story by generating coherence facts. The disclosed system may present the generated story via a graphical user interface.

Abstract: In implementations of systems for generating sequential supporting answer reports, a computing device implements a report system to receive a user input defining a question with respect to a visual representation of analytics data rendered in a user interface. The report system determines a final answer to the question by processing a semantic representation of the question using a machine learning model. A sequence of reports is generated and the sequence defines an order of progression from a first supporting answer to the final answer. Each report of the sequence of reports includes a visual representation of a supporting answer to the question. The report system displays a dashboard in the user interface including a first report of the sequence of reports, the first report depicting a visual representation of the first supporting answer to the question.

Abstract: In implementations of systems for generating sequential supporting answer reports, a computing device implements a report system to receive a user input defining a question with respect to a visual representation of analytics data rendered in a user interface. The report system determines a final answer to the question by processing a semantic representation of the question using a machine learning model. A sequence of reports is generated and the sequence defines an order of progression from a first supporting answer to the final answer. Each report of the sequence of reports includes a visual representation of a supporting answer to the question. The report system displays a dashboard in the user interface including a first report of the sequence of reports, the first report depicting a visual representation of the first supporting answer to the question.

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: The present disclosure relates to systems, non-transitory computer-readable media, and methods that utilize a data-driven approach to organize user-activity data for a user into a hierarchy of digital actions, digital tasks, and digital workflows and categorize a vector representing frequent activities from the hierarchy into a persona group for the user. From this vector representation, the disclosed systems can categorize the vector representation from among a distribution of other vector representations for other users into a persona group for the particular user. Based on at least one of the determined persona group or the vector representation, the disclosed systems can use a nodal graph to determine a digital recommendation that the particular user collaborate with other users or collaborate on a particular project.

Abstract: Techniques are provided for analyzing user actions that have occurred over a time period. The user actions can be, for example, with respect to the user's navigation of content or interaction with an application. Such user data is provided in an action string, which is converted into a highly searchable format. As such, the presence and frequency of particular user actions and patterns of user actions within an action string of a particular user, as well as among multiple action strings of multiple users, are determinable. Subsequences of one or more action strings are identified and both the number of action strings that include a particular subsequence and the frequency that a particular subsequence is present in a given action string are determinable. The conversion involves breaking that string into a sorted list of locations for the actions within that string. Queries can be readily applied against the sorted list.

Abstract: This disclosure covers methods, non-transitory computer readable media, and systems that use an intelligent analytics interface to process natural-language and other inputs to configure an analytics task for the system. The disclosed methods, non-transitory computer readable media, and systems provide the intelligent analytics interface to facilitate an exchange between the systems and a user to determine values for the analytics task. The methods, non-transitory computer readable media, and systems then use these values to execute an analytics task.

Abstract: Methods, systems, and computer storage media for providing command recommendations for an analysis-goal, using analytics system operations in an analytics systems. In operation, an analytics client is configured to provide an analytics interface for receiving a selection of analysis-goal information that corresponds to an analysis-goal model. A goal engine selects an analysis-goal based on the analysis-goal information. A command engine is configured to use the analysis-goal and goal-driven models to predict probable commands for the analysis goal. The command engine also selects a next command recommendation from the probable commands. The command engine generates additional command recommendation data based on a loss function fine tuner. The additional command recommendation data can include a goal orientation score that quantifies a degree to which a command aligns with the analysis-goal.

Abstract: In implementations of systems for generating sequential supporting answer reports, a computing device implements a report system to receive a user input defining a question with respect to a visual representation of analytics data rendered in a user interface. The report system determines a final answer to the question by processing a semantic representation of the question using a machine learning model. A sequence of reports is generated and the sequence defines an order of progression from a first supporting answer to the final answer. Each report of the sequence of reports includes a visual representation of a supporting answer to the question. The report system displays a dashboard in the user interface including a first report of the sequence of reports, the first report depicting a visual representation of the first supporting answer to the question.

Abstract: A method for recommending digital content includes: determining user preferences and a time horizon of a given user; determining a group for the given user based on the determined user preferences; determining a number of users of the determined group and a similarity of the users; applying information including the number of users, the similarity, and the time horizon to a model selection classifier to select one of a personalized model of the user and a group model of the determined group; and running the selected model to determine digital content to recommend.

Abstract: A machine-learning framework uses partial-click feedback to generate an optimal diverse set of items. An example method includes estimating a preference vector for a user based on diverse cascade statistics for the user, the diverse cascade statistics including previously observed responses and previously observed topic gains. The method also includes generating an ordered set of items from the item repository, the items in the ordered set having highest topic gain weighted by similarity with the preference vector, providing the ordered set for presentation to the user, and receiving feedback from the user on the ordered set. The method also includes, responsive to the feedback indicating a selected item, updating the diverse cascade statistics for observed items, wherein the updating results in penalizing the topic gain for items of the observed items that are not the selected item and promoting the topic gain for the selected item.

Abstract: A task-aware command recommendation system and related techniques are described herein. The task-aware command recommendation system can provide a user of a software application (e.g., an analytics application or other software application) with guidance by predicting commands that can be executed to accomplish a given task. For example, an ongoing task being performed by a user can be determined based on commands that have been performed by the user up to a current point in time. Information about the task can be incorporated into one or more command recommendation models, which can determine one or more commands to recommend to the user for performing the task. In some cases, the task-aware command recommendation system can include a help prediction model that can anticipate when the user is having difficulties completing a task, and can provide help for the user to continue performing the task.