UCSD's Reddit2Deezer dataset brings 190k real music chat pairs

Conversational music recommendation (CMR) research has long been stuck between two imperfect options: authentic conversation corpora that are too small to scale, and large synthetic corpora that feel artificial. A new dataset from UCSD researchers Haven Kim and Julian McAuley aims to break that deadlock. Reddit2Deezer extracts 190k unique {thread, leaf-comment} pairs from real Reddit discussions about music, then links each mentioned song or artist to a Deezer identifier. This gives researchers instant access to audio previews and metadata like genre tags, popularity scores, and BPM, making the dataset suitable for content-grounded CMR systems.

The dataset comes in two versions: a raw version that preserves the original Reddit text for maximum authenticity, and a carefully paraphrased version designed to remove personally identifiable information while keeping musical intent intact — enabling long-term reproducibility without privacy concerns. A human validation study confirmed that both the dialogues, the item grounding, and the paraphrases are high quality. By providing a large-scale, reality-grounded resource, Reddit2Deezer opens the door to training recommendation models that understand how real people talk about music, and could lead to more natural voice assistants and playlist curators.