Analytics personalization framework

Abstract

Methods, systems, and computer readable medium for personalizing an analytics user interface. The method includes generating a set of training data from received user interaction data, inputting the set of training data to a machine learning model to train the model, generating a set of user interest scores for the particular user that each indicate a user's interest in accessing information corresponding to a UI element of the application, determining, from the user interest scores, that the user is interested in a particular UI element that was not included in the initial UI and has at least a threshold score, dynamically modifying the initial UI to include the particular UI element, presenting the updated UI, monitoring further user interactions, updating the model based on the further user interactions, and modifying the updated UI based on the updated model.

1 - 20 . (canceled) 
     
     
         21 . A method performed by a computing device, the method comprising:
 generating, using a trained machine learning model, a set of user interest scores for a user, wherein each user interest score is indicative of the user's interest in accessing information corresponding to a user interface (UI) element of an application, wherein the trained machine learning model is trained using a set of training data comprising a set of features related to user interactions with UI elements of one or more UIs of the application, the features being selected for training the trained machine learning model based on respective durations for which UI elements corresponding to the features were presented to one or more users;   determining that at least one user interest score of the set of user interest scores positively indicates the user's interest in accessing information corresponding to a particular UI element that was not included in an initial UI presented to the user;   in response to determining that the at least one user interest score of the set of user interest scores positively indicates the user's interest in accessing the information corresponding to the particular UI element, dynamically modifying the initial UI, including incorporating the particular UI element into the initial UI to generate an updated UI that includes the particular UI element that was not included in the initial UI;   presenting the updated UI that includes the particular UI element to the user;   monitoring, after presenting the updated UI to the user, further user interactions with the updated UI;   updating the trained machine learning model based on the further user interactions; and   selecting an updated set of UI elements that are presented in the updated UI based on the updated machine learning model.   
     
     
         22 . The method of  claim 21 , wherein the trained machine learning model is one of (i) a logistical regression model, (ii) a machine learning model that combines a wide linear model for memorization with a deep neural network for generalization across a plurality of features, or (iii) a long short-term memory model. 
     
     
         23 . The method of  claim 21 , further comprising determining that a particular user interest score of the set of user interest scores associated with a further UI element is less than a threshold score, wherein the further UI element is not displayed to the user in the updated UI based on determining that the particular user interest score associated with the further UI element is less than the threshold. 
     
     
         24 . The method of  claim 21 , further comprising receiving a set of user interaction data indicating a group of multiple different users' interactions with one or more UI elements;
 generating, from the set of user interaction data, a set of user group training data; and   training the trained machine learning model using the user group training data.   
     
     
         25 . The method of  claim 24 , wherein the set of user interaction data relates to two or more different topics, and each topic of the two or more different topics is assigned a different weight based on an importance of the topic to at least one of the multiple different users. 
     
     
         26 . The method of  claim 21 , further comprising:
 determining, for the user, that a user-specific machine learning model has at least a threshold confidence level after obtaining a sufficient amount of user interaction data for the user and training the user-specific machine learning model using the user interaction data for the user; and   switching, from the trained machine learning model to the user-specific machine learning model for one or more subsequent updates to the UI for the user in response to determining that the user-specific machine learning model has at least a threshold confidence level.   
     
     
         27 . The method of  claim 21 , wherein at least one of the initial UI or the updated UI is presented to the user by a game console. 
     
     
         28 . The method of  claim 21 , wherein the trained machine learning model is trained for the user, the method further comprising:
 identifying a second user that is determined to be similar to the user; and   selecting, using the trained machine learning model and/or the updated machine learning model, UI elements to present to the second user based on the second user being determined to be similar to the user.   
     
     
         29 . The method of  claim 21 , wherein the set of features related to the user interactions with the UI elements of the one or more UIs of the application comprises one or more of (i) features related to zooming to view content of particular UI elements, (ii) features related to marking particular content as being helpful, or (iii) features related to submitting a query for additional content. 
     
     
         30 . The method of  claim 21 , further comprising:
 identifying a particular UI element in which the user is determined to no longer have interest; and   preventing presentation of the particular UI element to the user for at least a specified period of time.   
     
     
         31 . The method of  claim 21 , further comprising:
 initially training the trained machine learning model based on user interaction data for the user;   identifying one or more second users that share a common user characteristic with the user; and   using the trained machine learning model to select UI elements for presentation to the one or more second users.   
     
     
         32 . A system comprising:
 one or more processors; and   a memory communicatively coupled to the one or more processors, the memory storing instructions which, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
 generating, using a trained machine learning model, a set of user interest scores for a user, wherein each user interest score is indicative of the user's interest in accessing information corresponding to a user interface (UI) element of an application, wherein the trained machine learning model is trained using a set of training data comprising a set of features related to user interactions with UI elements of one or more UIs of the application, the features being selected for training the trained machine learning model based on respective durations for which UI elements corresponding to the features were presented to one or more users; 
 determining that at least one user interest score of the set of user interest scores positively indicates the user's interest in accessing information corresponding to a particular UI element that was not included in an initial UI presented to the user; 
 in response to determining that the at least one user interest score of the set of user interest scores positively indicates the user's interest in accessing the information corresponding to the particular UI element, dynamically modifying the initial UI, including incorporating the particular UI element into the initial UI to generate an updated UI that includes the particular UI element that was not included in the initial UI; 
 presenting the updated UI that includes the particular UI element to the user; 
 monitoring, after presenting the updated UI to the user, further user interactions with the updated UI; 
 updating the trained machine learning model based on the further user interactions; and 
 selecting an updated set of UI elements that are presented in the updated UI based on the updated machine learning model. 
   
     
     
         33 . The system of  claim 32 , wherein the trained machine learning model is one of (i) a logistical regression model, (ii) a machine learning model that that combines a wide linear model for memorization with a deep neural network for generalization across a plurality of features, or (iii) a long short-term memory model. 
     
     
         34 . The system of  claim 32 , the operations further comprising:
 determining that a particular user interest score of the set of user interest scores associated with a further UI element is less than a threshold score, wherein the further UI element is not displayed to the user in the updated UI based on determining that the particular user interest score associated with the further UI element is less than the threshold.   
     
     
         35 . The system of  claim 32 , the operations further comprising:
 receiving a set of user interaction data indicating a group of multiple different users' interactions with one or more UI elements;   generating, from received set of user interaction data, a set of user group training data; and   training the trained machine learning model using the user group training data.   
     
     
         36 . The system of  claim 35 , wherein the set of user interaction data relates to two or more different topics, and each topic of the two or more different topics is assigned a different weight based on an importance of the topic to at least one of the multiple different users. 
     
     
         37 . The system of  claim 32 , the operations further comprising:
 determining, for the user, that a user-specific machine learning model has at least a threshold confidence level after obtaining a sufficient amount of user interaction data for the user and training the user-specific machine learning model using the user interaction data for the user; and   switching, from the trained machine learning model to the user-specific machine learning model for one or more subsequent updates to the UI for the user in response to determining that the user-specific machine learning model has at least a threshold confidence level.   
     
     
         38 . The system of  claim 32 , wherein the trained machine learning model is trained for the user, the operations further comprising:
 identifying a second user that is determined to be similar to the user; and   selecting, using the trained machine learning model and/or the updated machine learning model, UI elements to present to the second user based on the second user being determined to be similar to the user.   
     
     
         39 . The system of  claim 32 , wherein the set of features related to the user interactions with the UI elements of the one or more UIs of the application comprises one or more of (i) features related to zooming to view content of particular UI elements, (ii) features related to marking particular content as being helpful, or (iii) features related to submitting a query for additional content. 
     
     
         40 . A non-transitory computer-readable storage device storing instructions that when executed by one or more processors cause the one or more processors to perform operations comprising:
 generating, using a trained machine learning model, a set of user interest scores for a user, wherein each user interest score is indicative of the user's interest in accessing information corresponding to a user interface (UI) element of an application, wherein the trained machine learning model is trained using a set of training data comprising a set of features related to user interactions with UI elements of one or more UIs of the application, the features being selected for training the trained machine learning model based on respective durations for which UI elements corresponding to the features were presented to one or more users;   determining that at least one user interest score of the set of user interest scores positively indicates the user's interest in accessing information corresponding to a particular UI element that was not included in an initial UI presented to the user;   in response to determining that the at least one user interest score of the set of user interest scores positively indicates the user's interest in accessing the information corresponding to the particular UI element, dynamically modifying the initial UI, including incorporating the particular UI element into the initial UI to generate an updated UI that includes the particular UI element that was not included in the initial UI;   presenting the updated UI that includes the particular UI element to the user;   monitoring, after presenting the updated UI to the user, further user interactions with the updated UI;   updating the trained machine learning model based on the further user interactions; and   selecting an updated set of UI elements that are presented in the updated UI based on the updated machine learning model.