눈으로 듣는 음악 추천 시스템
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This document discusses techniques for music recommendation including matrix factorization, word2vec, and deep learning on audio data. It describes analyzing a dataset of 5 million songs classified by genre and segmented by attributes like popularity, loudness, and whether they are from the top 1000 songs. Models like matrix factorization and word2vec are used to generate song vectors and map songs in low dimensional space to power music recommendations.
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눈으로 듣는 음악 추천 시스템
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- 6. ~ Matrix Factorization Word2Vec Deep Learning on audio
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- 15. 1.5 5.6 User 18 DJ 3 / Song Vector .
- 16. 1.5 5.6 User 18 DJ 3 / Song Vector . Feedback CF(Collaboration Filter) Contents CBF(Contents Based Filtering)
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- 21. 40D Dense Vector - Matrix Completion via ALS - User/Item Latent Feature Feedback Log(8day) - 4.3M(User) X 2.1M(Song) - 388M None zero values (Song)
- 22. 40D Dense Vector - Matrix Completion via ALS - User/Item Latent Feature Feedback Log(8day) - 4.3M(User) X 2.1M(Song) - 388M None zero values (Song) (Song) Vector Vector .
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- 25. Low level Feature Genre Classification Network
- 26. Low level Feature Genre Classification Network(Song) Vector Vector .
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- 40. Sampling 5M -> 16K Song Meta Mapping , , Stream Song Segmentation Light, Medium, Heavy, Extreme Dimension Reduction 40D -> 2D
- 41. Sampling 5M -> 16K Song Meta Mapping , , Stream Song Segmentation Light, Medium, Heavy, Extreme Dimension Reduction 40D -> 2D 5M Songs Top 1,000 Songs 15,000 Songs Uniform Sampling 1,000 Songs + = 16,000 Songs
- 42. Sampling 5M -> 16K Song Meta Mapping , , Stream Song Segmentation Light, Medium, Heavy, Extreme Dimension Reduction 40D -> 2D t-SNE
- 43. Sampling 5M -> 16K Song Meta Mapping , , Popularity Song Segmentation Light, Medium, Heavy, Extreme Dimension Reduction 40D -> 2D
- 44. Sampling 5M -> 16K Song Meta Mapping , , Popularity Song Segmentation Light, Medium, Heavy, Extreme Dimension Reduction 40D -> 2D
- 56. • Heavy • Top1000 Top1000 . • Popularity . • .
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- 63. Top1000 Top1000
- 67. • • MF • Top1000 Top1000 .
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