An Empirical Study on Zero-Data Bootstrapping for Conversational Recommender Systems
Summary
This paper presents an empirical study on bootstrapping conversational recommender systems using synthetic data generated from non-conversational signals, demonstrating that it outperforms zero-shot and scarce real-data methods in low-resource settings.
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Paper page - An Empirical Study on Zero-Data Bootstrapping for Conversational Recommender Systems
Source: https://huggingface.co/papers/2504.15476
Abstract
Non-conversational domain signals can generate synthetic dialogue data that outperforms zero-shot and scarce real-data baselines for bootstrapping conversational recommender systems.
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 ofzero-data CRS bootstrapping: generatingsynthetic conversational supervisionfrom 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 diversityandFisher information, across domain signals, model architectures, datasets, andfine-tuning paradigms. Our results show that domain-grounded synthetic data consistently outperforms zero-shot prompting and naive synthetic baselines;active selectionimproves data efficiency over random sampling; metadata andcollaborative filteringsignals 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/ .
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