Text2Tracks: Music Recommendation via Generative Retrieval
カートのアイテムが多すぎます
カートに追加できませんでした。
ウィッシュリストに追加できませんでした。
ほしい物リストの削除に失敗しました。
ポッドキャストのフォローに失敗しました
ポッドキャストのフォロー解除に失敗しました
Text2Tracks: Music Recommendation via Generative Retrieval
-
ナレーター：
-
著者：
このコンテンツについて
Natural language prompts are changing how we ask for music recommendations. Users want to say things like, "Recommend some old classics for slow dancing?". But traditional LLMs often just generate song titles, which has drawbacks like needing extra steps to find the actual track and being inefficient.
Introducing Text2Tracks from Spotify! This novel research tackles prompt-based music recommendation using generative retrieval. Instead of generating titles, Text2Tracks is trained to directly output relevant track IDs based on your text prompt.
A critical finding is that how you represent the track IDs makes a huge difference. Using semantic IDs derived from collaborative filtering embeddings proved most effective, significantly outperforming older methods like using artist and track names. This approach boosts effectiveness (48% increase in Hits@10) and efficiency (7.5x fewer decoding steps).
While developing effective ID strategies was a key challenge explored, Text2Tracks ultimately outperforms traditional retrieval methods, making it a powerful new model particularly suited for conversational recommendation scenarios.
Paper link: https://arxiv.org/pdf/2503.24193