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Embeat is a music recommendation system built on Spotify acoustic feature data. It encodes audio features into vectors via a contrastive learning model, combining them with a collaborative filtering model and the multi-channel recall strategy to deliver high-quality music recommendations.
Key Features:
- In-house models: EmbeatMLP encodes Spotify Audio Features (key, tempo, energy, mood, etc.) into 64-dimensional acoustic vectors, responsible for "sounding like"; Track2Vec learns co-occurrence patterns from millions of playlists, responsible for "what the public likes"
- Genre-aware: 6,291 micro-genre tags covering over 2 million artists are deeply integrated into the recommendation system, ensuring exceptionally stable performance for niche songs
- Blind-evaluated: Compared to Netease Cloud Music, it leads by a wide margin with an 84~95% win rate across 157 cross-language samples
- Multi-channel recall: 5 recall channels (Acoustic Similarity / Same-Genre Popular / Same Artist / Similar Artists / Playlist Collaborative Filtering), merged and scored for final output
- Flexible lookup: Supports retrieving seed tracks via Spotify track ID, ISRC, track title + artist name, or artist name alone
- Low-RAM support: Can be deployed on a VPS with 2GB+ RAM, responds in 30–200 ms, with multiple versions of the open-source database available
If you find this project helpful, please give it a ⭐️. It means a lot to a personal project, thanks!
- 2026-06-26: Open-source initial codebase + EmbeatMLP model weights
- 2026-06-26: Open-source 45M tracks dataset + Technical blog
- 2026-07-02: Open-source Qdrant database V1 [Google Drive] [Baidu Netdisk]
- 2026-09-06: Open-source Qdrant database V2 (same link above, 2GB+ RAM can deploy)
- 2026-09-06: Open-source Track2Vec model weights
- 2026-09-16: Open-source Evaluation data