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METHODS AND SYSTEMS FOR DYNAMICALLY SELECTING ALTERNATIVE CONTENT BASED ON REAL-TIME EVENTS DURING DEVICE SESSIONS USING CROSS-CHANNEL, TIME-BOUND DEEP REINFORCEMENT MACHINE LEARNING

Abstract:

Methods and systems are described herein for dynamically selecting alternative content based on real-time events during device sessions using a cross-channel, time-bound deep reinforcement machine learning. The use of this architecture allows for alternative content to be selected in a time-bound and continuous manner that provides predictions in a dynamic environment (e.g., an environment in which user data is continuously changing and new events are continuously occurring) and with an increased success rate (e.g., new data and events are factored into each prediction). For example, in the system each round of predictions considers both input features, which can change by a user's actions, state of a user interface, and/or previous responses and states.