METHOD FOR PREDICTING REACTIVENESS OF USERS OF MOBILE DEVICES FOR MOBILE MESSAGING

摘要

A method for predicting reactiveness of MMI users comprises:
reacting to a message with a mobile user device which is a receiver of the message,collecting ground-truth data (11) for a machine-learning classifier,extracting from the collected ground-truth data (11) a list of features (12) which determines a current or past context of the user, and each feature having a feature's prediction strength calculated as fraction of classes misclassified when removing the feature;selecting the list of features (12) based on each feature's prediction strength;defining a plurality of reactiveness classes (101); both the extracted list of features (12) and the reactiveness classes (101) being input to the machine-learning classifier;classifying (102) the user according to the defined reactiveness classes (101);predicting the user's reactiveness for the given current or past context of the user by determining the most likely reactiveness class via the machine-learning classifier.

主权项

1. A method for predicting reactiveness of users of mobile devices for messaging, the method comprising:
reacting to a message by a user with a mobile user device which is a receiver of the message, and being characterized by further comprising: collecting ground-truth data (11) for a machine-learning classifier, extracting a list of features (12) from the collected ground-truth data (11), the list of features (12) determining a current or past context of the user, and each feature having a feature's prediction strength calculated as a fraction of classes misclassified by the machine-learning classifier when removing the feature from the list of features (12); selecting the list of features (12) based on each feature's prediction strength; defining a plurality of reactiveness classes (101); both the extracted list of features (12) and the reactiveness classes (101) being input to the machine-learning classifier; classifying (102) the user by the machine-learning classifier in accordance with the defined reactiveness classes (101); predicting the user's reactiveness for the given current or past context of the user by determining the most likely reactiveness class via the machine-learning classifier.