Personalized next-best action recommendation with multi-party interaction learning for automated decision-making

TL;DR

This paper introduces a reinforced coupled recurrent neural network (CRN) that models complex multi-party interactions for personalized next-best action recommendations in dynamic decision-making scenarios.

Contribution

It proposes a novel deep learning framework that captures long-range, multi-sequence interactions between customers and decision-makers for personalized recommendations.

Findings

CRN effectively models complex multi-party interactions.

The approach improves decision-making by recommending optimal actions.

Demonstrates potential in personalized dynamic interventions.

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.