This is an automated message from the <a href=\"https://huggingface.co/librarian-bots\">Librarian Bot</a>. I found the following papers similar to this paper. </p>\n<p>The following papers were recommended by the Semantic Scholar API </p>\n<ul>\n<li><a href=\"https://huggingface.co/papers/2608.15949\">Ask to Be Sure: Informative Interactions for Confident Multi-Turn LLM Recommendation</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2608.06632\">Shape Your Feed: An LLM-based Agentic System for Conversational Recommendation</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2608.19735\">RecPFN: Prior-Fitted Networks for In-Context-Based Recommendations</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2608.23484\">Multi-Modal Semantic Expansion with Constrained LLM Reranking for Conversational Music Recommendation</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2609.00618\">Towards Effective Structured Context Modeling for Conversational Recommender Systems via Dual-node Monte Carlo Tree Search</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2607.24846\">Two Views, One Voice: Evidence-Grounded Conversational Music Recommendation</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2607.06765\">When and How to Ask: Dynamic Preference Elicitation Strategies for Conversational Recommendation</a> (2026)</li>\n</ul>\n<p> Please give a thumbs up to this comment if you found it helpful!</p>\n<p> If you want recommendations for any Paper on Hugging Face checkout <a href=\"https://huggingface.co/spaces/librarian-bots/recommend_similar_papers\">this</a> Space</p>\n<p> You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: <code>@librarian-bot recommend</code></p>\n","updatedAt":"2026-09-04T01:21:51.960Z","author":{"_id":"63d3e0e8ff1384ce6c5dd17d","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg","fullname":"Librarian Bot (Bot)","name":"librarian-bot","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":379,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.7112979292869568},"editors":["librarian-bot"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2504.15476","authors":[{"_id":"6a99faab8f7c3b755723937b","name":"Rohan Surana","hidden":false},{"_id":"6a99faab8f7c3b755723937c","name":"Junda Wu","hidden":false},{"_id":"6a99faab8f7c3b755723937d","name":"Zhouhang Xie","hidden":false},{"_id":"6a99faab8f7c3b755723937e","name":"Yu Xia","hidden":false},{"_id":"6a99faab8f7c3b755723937f","name":"Nathan Kallus","hidden":false},{"_id":"6a99faab8f7c3b7557239380","name":"Julian McAuley","hidden":false}],"publishedAt":"2026-08-28T00:00:00.000Z","submittedOnDailyAt":"2026-09-03T00:00:00.000Z","title":"An Empirical Study on Zero-Data Bootstrapping for Conversational Recommender Systems","submittedOnDailyBy":{"_id":"6620978a2fbb68cdc361725c","avatarUrl":"/avatars/0a6844c8fb1ad18461686dab412584c3.svg","isPro":false,"fullname":"Rohan Surana","user":"rohan2810","type":"user","name":"rohan2810"},"summary":"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. 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An Empirical Study on Zero-Data Bootstrapping for Conversational Recommender Systems
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 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/ .
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