Optimizing Conversational Product Recommendation via Reinforcement Learning
On the map: Deep reinforcement learning methods → RL training of language and agent models
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL training of language and agent models, one of 15 directions in Deep reinforcement learning methods. Builds on 19 of its 34 references that are in the map. First posted on arXiv 30 Jun 2025.
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19 of 34 references.
References · 19
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7 more
- 2023Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generationcited by 42
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- 2026Self-Distilled Reinforcement Learning for Co-Evolving Agentic Recommender Systems
- 2026Rethinking Recommendation Paradigms: From Pipelines to Agentic Recommender Systems
- 2024SAPIENT: Mastering Multi-turn Conversational Recommendation with Strategic Planning and Monte Carlo Tree Search
- 2025Research on Conversational Recommender System Considering Consumer Types
- 2023Lending Interaction Wings to Recommender Systems with Conversational Agents
- 2026Ask to Be Sure: Informative Interactions for Confident Multi-Turn LLM Recommendation
- 2026ChatShopBuddy: Towards Reliable Conversational Shopping Agents via Reinforcement Learning
- 2026HARPO: Hierarchical Agentic Reasoning for User-Aligned Conversational Recommendation