Active sample selection over review, metadata, and collaborative seed data plus LLM-generated synthetic dialogues improves fine-tuned conversational recommendation on ReDial and INSPIRED, though not uniformly across all settings.
Target-oriented Proactive Dialogue Systems with Personalization: Problem Formulation and Dataset Curation
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abstract
Target-oriented dialogue systems, designed to proactively steer conversations toward predefined targets or accomplish specific system-side goals, are an exciting area in conversational AI. In this work, by formulating a <dialogue act, topic> pair as the conversation target, we explore a novel problem of personalized target-oriented dialogue by considering personalization during the target accomplishment process. However, there remains an emergent need for high-quality datasets, and building one from scratch requires tremendous human effort. To address this, we propose an automatic dataset curation framework using a role-playing approach. Based on this framework, we construct a large-scale personalized target-oriented dialogue dataset, TopDial, which comprises about 18K multi-turn dialogues. The experimental results show that this dataset is of high quality and could contribute to exploring personalized target-oriented dialogue.
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2025 1verdicts
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From Reviews to Dialogues: Active Synthesis for Zero-Shot LLM-based Conversational Recommender System
Active sample selection over review, metadata, and collaborative seed data plus LLM-generated synthetic dialogues improves fine-tuned conversational recommendation on ReDial and INSPIRED, though not uniformly across all settings.