From Simulation to Strategy: Automating Personalized Interaction Planning for Conversational Agents
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
PersonalizedRouter: Personalized LLM Routing via Graph-based User Preference Modeling
The growing number of Large Language Models (LLMs) with diverse capabilities and response styles provides users with a wider range of choices, which presents challenges in selecting appropriate LLMs, as user preferences …
Vaiage: A Multi-Agent Solution to Personalized Travel Planning
Planning trips is a cognitively intensive task involving conflicting user preferences, dynamic external information, and multi-step temporal-spatial optimization. Traditional platforms often fall short - they provide sta…
Automating Gamification Personalization: To the User and Beyond
Personalized gamification explores knowledge about the users to tailor gamification designs to improve one-size-fits-all gamification. The tailoring process should simultaneously consider user and contextual characterist…
Recommendation SystemsKnowledge Boundary and Persona Dynamic Shape A Better Social Media Agent
Constructing personalized and anthropomorphic agents holds significant importance in the simulation of social networks. However, there are still two key problems in existing works: the agent possesses world knowledge tha…
World KnowledgeQuantEvolve: Automating Quantitative Strategy Discovery through Multi-Agent Evolutionary Framework
Automating quantitative trading strategy development in dynamic markets is challenging, especially with increasing demand for personalized investment solutions. Existing methods often fail to explore the vast strategy sp…