  Automated next-best action recommendation for each customer in a sequential,
dynamic and interactive context has been widely needed in natural, social and
business decision-making. Personalized next-best action recommendation must
involve past, current and future customer demographics and circumstances
(states) and behaviors, long-range sequential interactions between customers
and decision-makers, multi-sequence interactions between states, behaviors and
actions, and their reactions to their counterpart's actions. No existing
modeling theories and tools, including Markovian decision processes, user and
behavior modeling, deep sequential modeling, and personalized sequential
recommendation, can quantify such complex decision-making on a personal level.
We take a data-driven approach to learn the next-best actions for personalized
decision-making by a reinforced coupled recurrent neural network (CRN). CRN
represents multiple coupled dynamic sequences of a customer's historical and
current states, responses to decision-makers' actions, decision rewards to
actions, and learns long-term multi-sequence interactions between parties
(customer and decision-maker). Next-best actions are then recommended on each
customer at a time point to change their state for an optimal decision-making
objective. Our study demonstrates the potential of personalized deep learning
of multi-sequence interactions and automated dynamic intervention for
personalized decision-making in complex systems.
