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This study investigates zero-data bootstrapping for Conversational Recommender Systems (CRS) by generating synthetic conversational supervision from non-conversational signals such as item reviews and user-item interactions. The authors employ two information-theoretic selection strategies, Jensen-Shannon diversity and Fisher information, to evaluate the effectiveness of these synthetic data generation methods across various domains and model architectures. The key finding reveals that domain-grounded synthetic data not only surpasses zero-shot prompting and naive baselines but also demonstrates improved data efficiency in low-resource scenarios, highlighting the potential of non-conversational signals in CRS development without requiring in-domain dialogue data.
Synthetic data from non-conversational signals can outperform real dialogue in low-resource Conversational Recommender Systems, transforming how we approach data scarcity.
Conversational Recommender Systems (CRS) typically require domain-specific dialogue data, which is costly, scarce, and often unavailable in new domains. We conduct a systematic empirical study of zero-data CRS bootstrapping: generating synthetic conversational supervision from non-conversational signals---item reviews, metadata, and user-item interactions---without any in-domain dialogue corpus. We compare two information-theoretic selection strategies, Jensen-Shannon diversity and Fisher information, across domain signals, model architectures, datasets, and fine-tuning paradigms. Our results show that domain-grounded synthetic data consistently outperforms zero-shot prompting and naive synthetic baselines; active selection improves data efficiency over random sampling; metadata and collaborative filtering signals each improve selection quality; and, in low-resource settings, synthetic data can outperform scarce real dialogues while further complementing them. These findings establish non-conversational domain signals as a viable path toward building CRS without conversational training data. The code is available at https://anonymous.4open.science/r/zero_data_crs/ .
