Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for determining attention based on user interactions. While content is being presented by a client device, an attention determination system causes presentation of a prompt object on a display of the client device. The prompt object starts from an entry point and traverses a path across the display. While the prompt object is traversing the path across the display, the attention determination system detects a user input at a point on the display that is within a threshold distance of a current position of the prompt object along the path. The attention determination system determines an attention level of a user of the client device based on the user input.

Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for differential bid generation using machine learning. A bid management system generates an input based on features of a content item and. The bid management system uses the input to determine acceptance probability score distributions for a set of candidate users. The bid management system uses the acceptance probability score distributions along with user attributes describing the set of candidate users along with campaign parameters to determine a subset of the candidate users to receive an offer to view the content item along with bid values to offer to each candidate user.

Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for determining user engagement with a content item. A computing device accesses at least one image of eyes of a user that is captured while a client device is presenting a first content item on a display of the client device. The computing device determines, based on using the at least one image as input in a neural network, a gaze of the user. The gaze including coordinates at which the user is looking in relation to the client device. The neural network was trained based on machine generated images of a modeled human user looking at various coordinates. The computing device determines, based on the gaze of the user, an engagement score for the user. The engagement score indicates a level of engagement of the user with the first content item.

Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for differential bid generation using machine learning. A bid management system generates an input based on features of a content item and. The bid management system uses the input to determine acceptance probability score distributions for a set of candidate users. The bid management system uses the acceptance probability score distributions along with user attributes describing the set of candidate users along with campaign parameters to determine a subset of the candidate users to receive an offer to view the content item along with bid values to offer to each candidate user.

Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for determining attention based on user interactions. While content is being presented by a client device, an attention determination system causes presentation of a prompt object on a display of the client device. The prompt object starts from an entry point and traverses a path across the display. While the prompt object is traversing the path across the display, the attention determination system detects a user input at a point on the display that is within a threshold distance of a current position of the prompt object along the path. The attention determination system determines an attention level of a user of the client device based on the user input.

Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for determining user engagement with a content item. A computing device accesses at least one image of eyes of a user that is captured while a client device is presenting a first content item on a display of the client device. The computing device determines, based on using the at least one image as input in a neural network, a gaze of the user. The gaze including coordinates at which the user is looking in relation to the client device. The neural network was trained based on machine generated images of a modeled human user looking at various coordinates. The computing device determines, based on the gaze of the user, an engagement score for the user. The engagement score indicates a level of engagement of the user with the first content item.

Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for determining user engagement with a content item. A computing device accesses at least one image of eyes of a user that is captured while a client device is presenting a first content item on a display of the client device. The computing device determines, based on using the at least one image as input in a neural network, a gaze of the user. The gaze including coordinates at which the user is looking in relation to the client device. The neural network was trained based on machine generated images of a modeled human user looking at various coordinates. The computing device determines, based on the gaze of the user, an engagement score for the user. The engagement score indicates a level of engagement of the user with the first content item.

Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for determining user engagement with a content item. A computing device accesses at least one image of eyes of a user that is captured while a client device is presenting a first content item on a display of the client device. The computing device determines, based on using the at least one image as input in a neural network, a gaze of the user. The gaze including coordinates at which the user is looking in relation to the client device. The neural network was trained based on machine generated images of a modeled human user looking at various coordinates. The computing device determines, based on the gaze of the user, an engagement score for the user. The engagement score indicates a level of engagement of the user with the first content item.