Music Recommender Systems
•Télécharger en tant que PPTX, PDF•
8 j'aime•1,096 vues
Algorithm about recommender systems.
Recommandé
Recommandé
Contenu connexe
En vedette
En vedette (20)
Similaire à Music Recommender Systems
Similaire à Music Recommender Systems (20)
Dernier
Dernier (20)
Music Recommender Systems
- 1. Music Recommender Systems 超群.com fuchaoqun@gmail.com http://www.fuchaoqun.com
- 2. Who is Using Recommender Systems?
- 5. Keywords：recommender systems、 association rules 、collaborative filtering、slope one、 SVD、KNN....
- 7. Slope one
- 8. SVD
- 10. Slope one
- 11. SVD
- 14. Confidence:
- 19. Slope one
- 20. SVD
- 24. Simper Could Be Better
- 28. Slope one
- 29. SVD
- 31. SVD Image copy from Here
- 32. SVD In Image Compression Original K=10 K=20
- 33. Process SVD Define the original user-item matrix, R, of size m x n, which includes the ratings of m users on n items. rij refers to the rating of user ui on item ij . Preprocess user-item matrix R in order to eliminate all missing data values. Compute the SVD of R and obtain matrices U, S and V , of size m x m, m x n, and n x n, respectively. Their relationship is expressed by: R =U * S * VT . Perform the dimensionality reduction step by keeping only k diagonal entries from matrix S to obtain a k x k matrix, Sk. Similarly, matrices Uk and Vk of size m x k and k x n are generated. The "reduced" user-item matrix, R’, is obtained by R’ = Uk * Sk * VkT, while r'ij denotes the rating by user ui on item ij as included in this reduced matrix. Compute sqrt(Sk) and then calculate two matrix products: Uk * sqrt(Sk)T, which represents m users and sqrt(Sk) * VkT , which represents n items in the k dimen-sional feature space. We are particularly interested in the latter matrix, of size k x n. Use KNN on user matrix and item matrix, or you can multiply them to get user's rating on every item.
- 34. Demo Which two people have the most similar tastes? Which two season are the most close? from Here
- 35. Demo
- 36. Demo
- 38. matlab
- 43. ……
- 44. KNN:
- 45. matlab
- 46. FLANN
- 47. ……
- 48. All in one solution:
- 49. DIVISI
- 52. Data Cleaning
- 54. Data Mining
- 56. User collection
- 57. User listen log
- 58. User view log
- 60. Wrong data
- 61. Noise data
- 62. Duplicate data
- 64. Data Mining
- 66. User view and click what he like
- 68. Data Mining