Abstract: Disclosed herein is a method and system for collecting attentiveness information associated with a user's response to consuming a piece of media content. The attentiveness information is used to create an attentiveness-labelled behavioural data for the user's response. A computer-implemented attentiveness model may be generated by applying machine learning techniques to the a set of attentiveness-labelled behavioural data from multiple users. The system may comprise an annotation tool that facilitates human labelling of the user's response with attentiveness data. The resulting attentiveness model is therefore based on correlations indicative of attentiveness within the attentiveness-labelled behavioural data and/or physiological data that are based on real human cognition rather than a predetermined feature or combination of features.

Abstract: Disclosed herein is a method and system for collecting attentiveness information associated with a user's response to consuming a piece of media content. The attentiveness information is used to create an attentiveness-labelled behavioural data for the user's response. A computer-implemented attentiveness model may be generated by applying machine learning techniques to the a set of attentiveness-labelled behavioural data from multiple users. The system may comprise an annotation tool that facilitates human labelling of the user's response with attentiveness data. The resulting attentiveness model is therefore based on correlations indicative of attentiveness within the attentiveness-labelled behavioural data and/or physiological data that are based on real human cognition rather than a predetermined feature or combination of features.

Abstract: Disclosed herein is a method and system for collecting attentiveness information associated with a user's response to consuming a piece of media content. The attentiveness information is used to create an attentiveness-labelled behavioural data for the user's response. A computer-implemented attentiveness model may be generated by applying machine learning techniques to the a set of attentiveness-labelled behavioural data from multiple users. The system may comprise an annotation tool that facilitates human labelling of the user's response with attentiveness data. The resulting attentiveness model is therefore based on correlations indicative of attentiveness within the attentiveness-labelled behavioural data and/or physiological data that are based on real human cognition rather than a predetermined feature or combination of features.

Abstract: Disclosed herein is a method and system for collecting attentiveness information associated with a user's response to consuming a piece of media content. The attentiveness information is used to create an attentiveness-labelled behavioural data for the user's response. A computer-implemented attentiveness model may be generated by applying machine learning techniques to the a set of attentiveness-labelled behavioural data from multiple users. The system may comprise an annotation tool that facilitates human labelling of the user's response with attentiveness data. The resulting attentiveness model is therefore based on correlations indicative of attentiveness within the attentiveness-labelled behavioural data and/or physiological data that are based on real human cognition rather than a predetermined feature or combination of features.

Abstract: Disclosed herein is a method and system for collecting attentiveness information associated with a user's response to consuming a piece of media content. The attentiveness information is used to create an attentiveness-labelled behavioural data for the user's response. A computer-implemented attentiveness model may be generated by applying machine learning techniques to the a set of attentiveness-labelled behavioural data from multiple users. The system may comprise an annotation tool that facilitates human labelling of the user's response with attentiveness data. The resulting attentiveness model is therefore based on correlations indicative of attentiveness within the attentiveness-labelled behavioural data and/or physiological data that are based on real human cognition rather than a predetermined feature or combination of features.

Abstract: Disclosed herein is a method and system for collecting attentiveness information associated with a user's response to consuming a piece of media content. The attentiveness information is used to create an attentiveness-labelled behavioural data for the user's response. A computer-implemented attentiveness model may be generated by applying machine learning techniques to the a set of attentiveness-labelled behavioural data from multiple users. The system may comprise an annotation tool that facilitates human labelling of the user's response with attentiveness data. The resulting attentiveness model is therefore based on correlations indicative of attentiveness within the attentiveness-labelled behavioural data and/or physiological data that are based on real human cognition rather than a predetermined feature or combination of features.