用强化学习优化对话推荐策略，提升跨行业转化率
Optimizing Conversational Product Recommendation via Reinforcement Learning
- 基于反馈驱动的强化学习，让智能体自主优化对话时机与方式
- 通过行为模式挖掘，提升用户参与度与产品采纳率
- 适合需要个性化推荐的电商、客服等企业场景
我们提出一种基于强化学习的方法，用于优化跨行业的对话式产品推荐策略。随着智能代理在销售与服务中广泛应用，对话效果不仅取决于推荐内容，更取决于推荐的时机与方式。本文提出让代理系统通过反馈驱动的强化学习，自主学习最优对话策略。通过挖掘用户行为模式与转化结果，该方法使代理能够持续优化沟通话术，提升用户参与度和产品采纳率，同时遵守上下文与监管约束。我们构建了概念框架，揭示关键创新，并讨论其在企业级可扩展、个性化推荐中的应用前景。
原文摘要 · Abstract (English)
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.
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