Prompt Optimization for User Simulation in Conversational Recommender Systems: A Multi-Objective Framework
arXiv:2607.00010v1 Announce Type: cross Abstract: Conversational recommender systems (CRSs) are a core component of next-generation intelligent recommender systems because they enable users to actively elicit preferences, clarify intentions, and adapt recommendations in real time. However, there are two key obstacles in the CRS domain: evaluation and access to training data. Evaluating CRSs through real human studies is more critical than for traditional recommender systems, yet such studies are both costly and time-consuming. Moreover, CRS interaction data are often difficult to obtain for mo
The increasing sophistication of AI models enables more realistic user simulation, addressing long-standing data and evaluation bottlenecks in conversational AI development.
This research provides a scalable method to evaluate and develop conversational AI systems, sidestepping costly and slow human studies, which accelerates their deployment and refinement.
The ability to simulate user interactions effectively changes the development paradigm for conversational recommender systems, allowing for faster iteration and improvement without extensive real-world testing.
- · AI developers
- · e-commerce platforms
- · customer service providers
- · AI agents
- · Traditional human evaluation services
- · Companies unable to leverage advanced simulation
Faster development and deployment of more effective conversational AI.
Increased adoption of AI-powered conversational interfaces across industries due to lower development costs and improved quality.
Enhanced personalization and automation of online services, potentially leading to a significant shift in user interaction paradigms.
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Read at arXiv cs.CL