An Empirical Study on Zero-Data Bootstrapping for Conversational Recommender Systems

Rohan Surana, Junda Wu, Zhouhang Xie, Yu Xia, Nathan Kallus, Julian McAuley

An Empirical Study on Zero-Data Bootstrapping for Conversational Recommender Systems: 1 upvotes on Hugging Face Daily Papers, #36 of 36 papers on 2026-09-03. Day-by-day upvote history.

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/ .

Paper page on Hugging Face · arXiv

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