Personalized predictive models

A method for improving the functionality of a computing device on which is operated a email application with functionality requiring user action on received emails, comprising:

• receiving, by the email application executing on the computing device, an email, wherein the email comprises two or more features characterizing the email;
• presenting, on a user interface of the computing device, the email to a user for a user decision;
• predicting the user decision on the received email via a generic predictive model operated by the computing device, wherein the generic predictive model utilizes a generic precision/recall curve for obtaining a user decision threshold for determining a percentage of received emails to be decided according to a prescribed decision by the user, wherein the generic precision/recall curve is generated from at least another user's response to a same or similar email, and wherein which generic precision/recall curve is utilized may be based, at least in part, on the two or more features;
• comparing an actual user decision on the received email with the predicted user decision for determining an accuracy of the predicted user decision;
• updating one or more features of the generic predictive model for personalizing the generic predictive model as a personalized predictive model for a user of the received email;
• performing a computational loop for a precision recall curve associated each of two or more potential thresholds to determine best threshold value for the personalized predictive model;
• generating the personalized predictive model based on the one or more updated features for predicting a decision of the user on one or more future emails;
• and
• applying the personalized predictive model to move automatically an email message to a junk folder without input from the user.

A method for predicting a user's decisions on received emails;

• comprising:
• receiving via a email application an email requiring a decision by a receiving user, wherein the email comprises two or more features characterizing the email;
• presenting, on a user interface of the computing device, the email to a user for the user decision;
• using a generic predictive model, predicting the user decision on a received email, wherein the generic predictive model utilizes a generic precision/recall curve for obtaining a user decision threshold for determining a percentage of received emails to be decided according to a prescribed decision by the user, wherein the generic precision/recall curve is generated from at least another user's response to a same or similar email, and wherein which generic precision/recall curve is utilized may be based, at least in part, on the two or more features;
• performing a computational loop for a precision recall curve associated with each of two or more potential thresholds to determine best threshold value for the personalized predictive model;
• comparing an actual user decision on the received email with the predicted user decision for determining an accuracy of the predicted user decision;
• generating a personalized predictive model by updating one or more features of the generic predictive model for personalizing the generic predictive model as a personalized predictive model for a user of the received email;
• using the personalized predictive model for predicting a user decision for each of a plurality of future emails;
• based on the prediction, moving automatically an email message to a junk folder without input from the user
• after a prescribed duration of time, comparing an actual user decision for each of the plurality of future emails with corresponding predicted user decisions for each of the plurality of future emails;
• and
• updating the personalized predictive model for more accurately predicting a user decision for each of a plurality of additional future emails.