Abstract: The present disclosure describes automated generation of early warning predictive insights derived from contextual analysis of user activity data of a distributed software platform. Predictive insights are automatically generated from analysis of user activity through implementation of trained artificial intelligence (AI) modeling. User activity data is accessed pertaining to user interactions by a plurality of users on a software data platform. The trained AI modeling generates a plurality of mobility determinations that identify changes in patterns of user behavior over a current temporal filter associated with the user activity data. The plurality of mobility determinations is curated using business logic rules that evaluate a relevance of the mobility determinations. One or more predictive insights may be generated and presented via a graphical user interface notification.

Abstract: The present disclosure describes automated generation of early warning predictive insights derived from contextual analysis of user activity data of a distributed software platform. Predictive insights are automatically generated from analysis of user activity through implementation of trained artificial intelligence (AI) modeling. User activity data is accessed pertaining to user interactions by a plurality of users a software data platform. The trained AI modeling generates a plurality of mobility determinations that identify changes in patterns of user behavior over a current temporal filter associated with the user activity data. The plurality of mobility determinations is curated using business logic rules that evaluate a relevance of the mobility determinations. One or more predictive insights may be generated and presented via a graphical user interface notification.

Abstract: Non-limiting examples of the present disclosure relate to application of artificial intelligence (AI) processing to generate classifications of user activity for a group of users. For example, a classification prediction is generated indicating whether students in an educational class are predicted, over a predetermined time period, to have a high or low activity level based on contextual analysis of multiple types of user-driven events. As user activity data is typically quite robust, the present disclosure applies dimensionality reduction processing to efficiently manage user activity data and further improve accuracy in generating downstream binary classifications. A dimensionality reduction transformation of user activity data results in a low-dimensional representation of input feature data that is contextually relevant for generating a binary classification. Derived classifications are then utilized to generate data insights pertaining to user activity levels of one or more users.

Abstract: The present disclosure describes automated generation of early warning predictive insights derived from contextual analysis of user activity data of a distributed software platform. Predictive insights are automatically generated from analysis of user activity through implementation of trained artificial intelligence (AI) modeling. User activity data is accessed pertaining to user interactions by a plurality of users a software data platform. The trained AI modeling generates a plurality of mobility determinations that identify changes in patterns of user behavior over a current temporal filter associated with the user activity data. The plurality of mobility determinations is curated using business logic rules that evaluate a relevance of the mobility determinations. One or more predictive insights may be generated and presented via a graphical user interface notification.

Abstract: A playlist generator that utilizes multiple data sources to rank each track within a set of candidate tracks to enable selection of candidate tracks according to the ranking. Candidate tracks are each scored according to one or more features, such as acoustic similarity and/or similar usage patterns of the candidate track or artist of the candidate track to a current or previously played track or artist. Each feature is weighted according to historical listening patterns surrounding a user-selected playlist seed artist. The weighting may also be further corrected according to historical listening patterns of the particular user. When historical usage data related to a particular seed artist is limited, more generalized historical usage data related to a higher level in a genre hierarchy may be used.

Abstract: A modern, personalized, adaptive learning experience may be enabled for distinct groups of students. Content entered in a notebook application or similar platform may be analyzed. Content from a learning object repository may then be selected to be suggested based on comparison with the entered content. A style may also be determined based on one or more of a common attribute of a group of teachers, a common attribute of a group of students, or a rule of an organization. The selected content to be suggested may be automatically customized to conform to the style and a lesson plan, and the customized content may be provided to a client application or another service to be displayed in conformance with the lesson plan to students supporting teachers by freeing teachers' time through optimization of the learning process, creation of easy and simple to use experiences, and actionable analytics and proactive alerts.

Abstract: Example apparatus and methods access multiple sources of information concerning features for applications, clean the data from the multiple sources, extract features from the cleaned data, selectively weight the sources, data or extracted features and produce a feature vector. The feature vector may then be used in a single language feature space or in a multi-language feature space. Feature spaces may then be used to find similarities between applications to facilitate recommending applications. In one embodiment, different feature spaces may be connected using a graph where nodes represent items and edges represent similarity relationships between items based on related feature spaces. Traversing the graph may allow similarities to be found that might not otherwise be possible. For example, while there may be no direct English to Hebrew similarity relationship, there may be English to French and French to Hebrew relationships that can be followed in the graph.

Abstract: A playlist generator that utilizes multiple data sources to rank each track within a set of candidate tracks to enable selection of candidate tracks according to the ranking. Candidate tracks are each scored according to one or more features, such as acoustic similarity and/or similar usage patterns of the candidate track or artist of the candidate track to a current or previously played track or artist. Each feature is weighted according to historical listening patterns surrounding a user-selected playlist seed artist. The weighting may also be further corrected according to historical listening patterns of the particular user. When historical usage data related to a particular seed artist is limited, more generalized historical usage data related to a higher level in a genre hierarchy may be used.

Abstract: A method for producing item recommendations for user consumption from usage data of items as they are consumed in combinations or baskets; breaking the baskets into positive pairs of items appearing in the baskets; finding negative pairs of items appearing relatively frequently in the baskets but not in the positive pairs; embedding all the items of the global catalog or universal set into a latent space such that items appearing together more often in the positive pairs are relatively close together and items appearing together in the negative pairs are relatively far apart; obtaining a selection from a user of a first item for consumption and providing to the user at least one suggestion of a second item for further consumption, the second item not being identical with the first item, the second item being an item located most closely in the latent space to the first item for consumption.

Abstract: Example apparatus and methods access multiple sources of information concerning features for applications, clean the data from the multiple sources, extract features from the cleaned data, selectively weight the sources, data or extracted features and produce a feature vector. The feature vector may then be used in a single language feature space or in a multi-language feature space. Feature spaces may then be used to find similarities between applications to facilitate recommending applications. In one embodiment, different feature spaces may be connected using a graph where nodes represent items and edges represent similarity relationships between items based on related feature spaces. Traversing the graph may allow similarities to be found that might not otherwise be possible. For example, while there may be no direct English to Hebrew similarity relationship, there may be English to French and French to Hebrew relationships that can be followed in the graph.

Abstract: Example apparatus and methods produce an explanation of why a recommendation is being made by an automated collaborative filtering recommendation system. The explanation may include feature categories and features that describe the item being recommended. The feature categories selected and features selected may depend on a personalization level for an item associated with the recommendation, a quality level of the descriptiveness of a feature for the recommendation, and correlations between items and features analyzed by the recommendation system. The feature categories and features may be selected based on an aggregate score that considers and combines the personalization level, the quality level, and the correlations. The quality level may be human curated or may vary directly with the ability of a feature to partition a feature space. Correlations between items and features reflect the degree to which the features are exhibited by the items.