Reddit2Deezer: A Scalable Dataset for Real-World Grounded Conversational Music Recommendation
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866915997927079936 |
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| author | Kim, Haven McAuley, Julian |
| author_facet | Kim, Haven McAuley, Julian |
| contents | Conversational music recommendation (CMR) research currently faces a tradeoff between authentic dialogue corpora that are limited in scale and synthesized corpora that scale up but whose conversations are artificially constructed rather than naturally observed. In this paper, we introduce Reddit2Deezer, a reality-grounded CMR resource derived from 190k unique {thread, leaf-comment} pairs. We release the resource in two versions: a raw version that preserves authenticity, and a paraphrased version that maximizes long-term reproducibility. Each musical entity is linked to a Deezer identifier, which provides straightforward access to audio previews and rich metadata (e.g., genre tags, popularity, BPM), opening the door to future research on content-grounded conversational recommendation. A human validation confirms the quality of the dialogues, item grounding, and paraphrases. The dataset is available at https://huggingface.co/datasets/McAuley-Lab/Reddit2Deezer. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_09120 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Reddit2Deezer: A Scalable Dataset for Real-World Grounded Conversational Music Recommendation Kim, Haven McAuley, Julian Information Retrieval Sound Conversational music recommendation (CMR) research currently faces a tradeoff between authentic dialogue corpora that are limited in scale and synthesized corpora that scale up but whose conversations are artificially constructed rather than naturally observed. In this paper, we introduce Reddit2Deezer, a reality-grounded CMR resource derived from 190k unique {thread, leaf-comment} pairs. We release the resource in two versions: a raw version that preserves authenticity, and a paraphrased version that maximizes long-term reproducibility. Each musical entity is linked to a Deezer identifier, which provides straightforward access to audio previews and rich metadata (e.g., genre tags, popularity, BPM), opening the door to future research on content-grounded conversational recommendation. A human validation confirms the quality of the dialogues, item grounding, and paraphrases. The dataset is available at https://huggingface.co/datasets/McAuley-Lab/Reddit2Deezer. |
| title | Reddit2Deezer: A Scalable Dataset for Real-World Grounded Conversational Music Recommendation |
| topic | Information Retrieval Sound |
| url | https://arxiv.org/abs/2605.09120 |