Abstract
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.
Highlights
In enterprise and complex problem-solving, automated and personalized decision-making is highly needed but rarely possible in practice
A backtesting of our personalized next-best action recommendation was conducted on fiveyear (2012-2017) debt collection data in a major Australian government agency
The data comprises attributes about client demographics and circumstances, the debt amount and duration at each time point associated with a debtor, a list of optional debt collection actions and their application policy constraints, a sequence of historical actions taken by the government on a debtor to recover the debt at each time point, the corresponding client response behavior to each debt collection action, and the time information associated with debt cases, responses and actions
Summary
In enterprise and complex problem-solving, automated and personalized decision-making is highly needed but rarely possible in practice. Personalized decision-making requires personalized next-best actions to be learned and used in a dynamic, sequential and interactive process and context, which is extremely demanding in both private and public sectors and natural and social systems. Examples are next-best treatments to-be-made by healthcare providers on patients, next-best trading strategies to-be-taken by investors in a capital market, next-best interventions on cybersecurity attacks or climate change in real time, next-best. Personalizing next-best action recommendation for automated decision-making
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