From Simulation to Strategy: Automating Personalized Interaction Planning for Conversational Agents
This work addresses the challenge of personalizing interaction planning for sales-oriented conversational agents, though it appears incremental as it builds on existing user-simulator studies with a specific focus on occupation-based adaptation.
The paper tackled the problem of tuning effective conversation strategies for sales-oriented conversational agents by investigating how user profiles (age, gender, occupation) influence dialogue performance, finding that occupation produces the most pronounced differences in conversational intent. They introduced a lightweight, occupation-conditioned strategy that guides the agent to prioritize intents aligned with user preferences, resulting in shorter and more successful dialogues.
Amid the rapid rise of agentic dialogue models, realistic user-simulator studies are essential for tuning effective conversation strategies. This work investigates a sales-oriented agent that adapts its dialogue based on user profiles spanning age, gender, and occupation. While age and gender influence overall performance, occupation produces the most pronounced differences in conversational intent. Leveraging this insight, we introduce a lightweight, occupation-conditioned strategy that guides the agent to prioritize intents aligned with user preferences, resulting in shorter and more successful dialogues. Our findings highlight the importance of rich simulator profiles and demonstrate how simple persona-informed strategies can enhance the effectiveness of sales-oriented dialogue systems.