Abstract: Systems, methods, and computer-readable media for delivering recommendations are provided to personalize user experience, optimize online advertising, and maximize revenue for online merchants. An example system can include a computer configured to: receive historic user online actions data and one or more purchase confirmations of a user, train a deep reinforcement learning system based on the received data, receive a current observation characterizing interaction of the user with at least one of the recommendations in an online environment, determine a reward for the deep reinforcement learning system based on the current observation, where the reward depends on a time parameter associated with an intended action of the user, select an action to be performed by an agent based on the reward, and cause the agent to provide or display a new recommendation to the user or another comparable user based on the selected action.

Abstract: An object recognition system and method is provided which uses automated algorithmically determined negative training. Negative training with respect to a particular object classifier allows for more streamlined and efficient targeted negative training, enabling time and cost savings while simultaneously improving the accuracy of recognition based on the targeted negative training.

Abstract: An object recognition system and method is provided which uses automated algorithmically determined negative training. Negative training with respect to a particular object classifier allows for more streamlined and efficient targeted negative training, enabling time and cost savings while simultaneously improving the accuracy of recognition based on the targeted negative training.

Abstract: A communications device is configured to allow a user to sort, organize, and manage a plurality of contacts in a contact list stored on the device. The device includes a display to display a multi-dimensional contact board. The user can move contact objects representing the contacts to desired locations on the multi-dimensional contact board. The device will categorize each contact object based on its location on the multi-dimensional contact board.