Optimizing Conversational Product Recommendation via Reinforcement Learning
This addresses the need for more effective conversational agents in sales and service operations across diverse industries, though it appears incremental in applying existing methods to this domain.
The paper tackles the problem of optimizing conversational strategies for product recommendation using reinforcement learning, resulting in agents that refine talk tracks to drive higher engagement and product uptake while adhering to constraints.
We propose a reinforcement learning-based approach to optimize conversational strategies for product recommendation across diverse industries. As organizations increasingly adopt intelligent agents to support sales and service operations, the effectiveness of a conversation hinges not only on what is recommended but how and when recommendations are delivered. We explore a methodology where agentic systems learn optimal dialogue policies through feedback-driven reinforcement learning. By mining aggregate behavioral patterns and conversion outcomes, our approach enables agents to refine talk tracks that drive higher engagement and product uptake, while adhering to contextual and regulatory constraints. We outline the conceptual framework, highlight key innovations, and discuss the implications for scalable, personalized recommendation in enterprise environments.