Interplay: Training Independent Simulators for Reference-Free Conversational Recommendation
This provides a scalable solution for generating high-quality training data for conversational recommender systems, addressing a bottleneck in data collection for researchers and developers in AI and recommendation domains.
The paper tackles the challenge of generating realistic dialogue data for conversational recommender systems by proposing a reference-free simulation framework that trains two independent LLMs as user and recommender, which interact without predetermined target items, resulting in more authentic conversations that match or exceed existing methods in quality.
Training conversational recommender systems (CRS) requires extensive dialogue data, which is challenging to collect at scale. To address this, researchers have used simulated user-recommender conversations. Traditional simulation approaches often utilize a single large language model (LLM) that generates entire conversations with prior knowledge of the target items, leading to scripted and artificial dialogues. We propose a reference-free simulation framework that trains two independent LLMs, one as the user and one as the conversational recommender. These models interact in real-time without access to predetermined target items, but preference summaries and target attributes, enabling the recommender to genuinely infer user preferences through dialogue. This approach produces more realistic and diverse conversations that closely mirror authentic human-AI interactions. Our reference-free simulators match or exceed existing methods in quality, while offering a scalable solution for generating high-quality conversational recommendation data without constraining conversations to pre-defined target items. We conduct both quantitative and human evaluations to confirm the effectiveness of our reference-free approach.