Optimizing Conversational Product Recommendation via Reinforcement Learning

TL;DR

This paper introduces a reinforcement learning framework for optimizing conversational strategies in product recommendation, aiming to enhance engagement and conversion rates in enterprise settings.

Contribution

It presents a novel reinforcement learning methodology for training dialogue policies that improve recommendation effectiveness in diverse industries.

Findings

Agents learn optimal talk tracks through feedback and behavioral data

Enhanced engagement and product uptake demonstrated

Framework supports scalable and personalized recommendations

Abstract

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.