Abstract: Systems and methods of the present disclosure provide dynamic and context sensitive user experience optimization by receiving an interface rendering request from a user device associated with a user and accessing user data of the user. A trained rendering type machine learning model is used to predict a rendering type for displaying a user interface (UI) to the user, based on the user data. A trained rendering template machine learning model is used to predict an interface rendering template for the UI rendering based on the rendering type and the user data. A trained presentation machine learning model is used to predict a presentation item to display in the rendering template based on the rendering type, and the user data. The user device is instructed to render the UI on a display so as to dynamically display a context sensitive UI including the presentation item according to the rendering template.

Abstract: In some embodiments, text for user consumption may be generated based on an intended user action category and a user profile. In some embodiments, an action category, a plurality of text seeds, and a profile comprising feature values may be obtained. Context values may be generated based on the feature values, and text generation models may be obtained based on the text seeds. In some embodiments, messages may be generated using the text generation models based on the action category and the context values. Weights associated with the messages may be determined, and a first text message of the messages may be sent to an address associated with the profile based on the weights. Based on a reaction value obtained in response to the first message, a first expected allocation value may be updated based on the reaction value.

Abstract: Systems and methods of the present disclosure provide dynamic and context sensitive user experience optimization by receiving an interface rendering request from a user device associated with a user and accessing user data of the user. A trained rendering type machine learning model is used to predict a rendering type for displaying a user interface (UI) to the user, based on the user data. A trained rendering template machine learning model is used to predict an interface rendering template for the UI rendering based on the rendering type and the user data. A trained presentation machine learning model is used to predict a presentation item to display in the rendering template based on the rendering type, and the user data. The user device is instructed to render the UI on a display so as to dynamically display a context sensitive UI including the presentation item according to the rendering template.

Abstract: Systems and methods of the present disclosure provide dynamic and context sensitive user experience optimization by receiving an interface rendering request from a user device associated with a user and accessing user data of the user. A trained rendering type machine learning model is used to predict a rendering type for displaying a user interface (UI) to the user, based on the user data. A trained rendering template machine learning model is used to predict an interface rendering template for the UI rendering based on the rendering type and the user data. A trained presentation machine learning model is used to predict a presentation item to display in the rendering template based on the rendering type, and the user data. The user device is instructed to render the UI on a display so as to dynamically display a context sensitive UI including the presentation item according to the rendering template.

Abstract: In some embodiments, text for user consumption may be generated based on an intended user action category and a user profile. In some embodiments, an action category, a plurality of text seeds, and a profile comprising feature values may be obtained. Context values may be generated based on the feature values, and text generation models may be obtained based on the text seeds. In some embodiments, messages may be generated using the text generation models based on the action category and the context values. Weights associated with the messages may be determined, and a first text message of the messages may be sent to an address associated with the profile based on the weights. Based on a reaction value obtained in response to the first message, a first expected allocation value may be updated based on the reaction value.

Abstract: In some embodiments, text for user consumption may be generated based on an intended user action category and a user profile. In some embodiments, an action category, a plurality of text seeds, and a profile comprising feature values may be obtained. Context values may be generated based on the feature values, and text generation models may be obtained based on the text seeds. In some embodiments, messages may be generated using the text generation models based on the action category and the context values. Weights associated with the messages may be determined, and a first text message of the messages may be sent to an address associated with the profile based on the weights. Based on a reaction value obtained in response to the first message, a first expected allocation value may be updated based on the reaction value.

Abstract: In some embodiments, text for user consumption may be generated based on an intended user action category and a user profile. In some embodiments, an action category, a plurality of text seeds, and a profile comprising feature values may be obtained. Context values may be generated based on the feature values, and text generation models may be obtained based on the text seeds. In some embodiments, messages may be generated using the text generation models based on the action category and the context values. Weights associated with the messages may be determined, and a first text message of the messages may be sent to an address associated with the profile based on the weights. Based on a reaction value obtained in response to the first message, a first expected allocation value may be updated based on the reaction value.

Abstract: In some embodiments, text for user consumption may be generated based on an intended user action category and a user profile. In some embodiments, an action category, a plurality of text seeds, and a profile comprising feature values may be obtained. Context values may be generated based on the feature values, and text generation models may be obtained based on the text seeds. In some embodiments, messages may be generated using the text generation models based on the action category and the context values. Weights associated with the messages may be determined, and a first text message of the messages may be sent to an address associated with the profile based on the weights. Based on a reaction value obtained in response to the first message, a first expected allocation value may be updated based on the reaction value.