Abstract: Training a machine learning model includes electronically retrieving feature vectors that comprise a electronic representations of multidimensional observations, each observation uniquely associated with a predetermined observation value. A multi-class data structure comprising a plurality of buckets is generated by binning the observation values associated with the multidimensional observations. Each bucket corresponds to a range of values and contains observations whose associated observation values lie within the range. A machine learning model is trained using the feature vectors to classify feature vector inputs, assigning each feature vector input to a bucket. Simulated execution of the machine learning model classifies simulation feature vectors by assigning each simulation feature vector to a bucket based on the feature.

Abstract: A client device can identify user data pertaining to use of the client device by a user. The client device can determine at least one persona trait of the user based on the user data pertaining to the use of the client device by the user. The client device can receive persona categorization data, the persona categorization data specifying a plurality of persona categories and, for each persona category, a plurality of persona traits. Based on the at least one determined persona trait, the client device can assign the user to a persona category selected from the plurality of persona categories. Based on the persona category to which the user is assigned, the client device can identify information to present to users who are assigned to the persona category to which the user is assigned. The client device can present to the user the identified information.

Abstract: Training a machine learning model includes electronically retrieving feature vectors that comprise a electronic representations of multidimensional observations, each observation uniquely associated with a predetermined observation value. A multi-class data structure comprising a plurality of buckets is generated by binning the observation values associated with the multidimensional observations. Each bucket corresponds to a range of values and contains observations whose associated observation values lie within the range. A machine learning model is trained using the feature vectors to classify feature vector inputs, assigning each feature vector input to a bucket. Simulated execution of the machine learning model classifies simulation feature vectors by assigning each simulation feature vector to a bucket based on the feature.

Abstract: A client device can identify user data pertaining to use of the client device by a user. The client device can determine at least one persona trait of the user based on the user data pertaining to the use of the client device by the user. The client device can receive persona categorization data, the persona categorization data specifying a plurality of persona categories and, for each persona category, a plurality of persona traits. Based on the at least one determined persona trait, the client device can assign the user to a persona category selected from the plurality of persona categories. Based on the persona category to which the user is assigned, the client device can identify information to present to users who are assigned to the persona category to which the user is assigned. The client device can present to the user the identified information.

Abstract: A method of providing a comparison-based ranked output includes extracting a plurality of pairwise comparisons between transaction objects, from a data set comprising transaction histories of one or more users' transactions with transaction objects over time. The method includes determining a plurality of user groups, wherein each user group of the plurality of user groups is associated with a common level feature among a set of transaction objects. For each transaction object, a present height associated with the transaction object based on the extracted plurality of pairwise comparisons is determined. Further, the method includes generating, based on a comparison of present heights of transaction objects associated with the common level feature, a data object comprising a ranking of transaction objects for a user group of the plurality of user groups, and providing the data object to an application to provide a user interface based on the ranking of transaction objects.

Abstract: Techniques for identifying potential uses of computing systems are disclosed. A potential use of a computing system can be identified by considering the context that effectively represents a situation for the computing system and further considering a known situation to be a match for the situation. As such, a potential state of use of the computing system can be identified based on one or more known states of use of the computing system. A matching situation can, for example, be determined, based on pattern of use data that effectively associates a state of use with a situation in which the use may or has occurred. Identifying a potential state of use, among other things, allows making various applications, tasks, and/or services more accessible and/or effectively recommending what is likely to be used for a given situation without requiring supervised training or direct feedback from a user.

Abstract: Techniques for identifying potential uses of computing systems are disclosed. A potential use of a computing system can be identified by considering the context that effectively represents a situation for the computing system and further considering a known situation to be a match for the situation. A known situation can be associated with a known corresponding state of use of the computing system. As such, a potential state of use of the computing system can be identified based on one or more known states of use of the computing system. A matching situation can, for example, be determined, based on pattern of use data that effectively associates a state of use with a situation in which the use may or has occurred. Identifying a potential state of use, among other things, allows making various applications, tasks, and/or services more accessible and/or effectively recommending what is likely to be used for a given situation.