Algorithm about recommender systems.

1. Music Recommender Systems 超群.com fuchaoqun@gmail.com http://www.fuchaoqun.com

2. Who is Using Recommender Systems?

4. http://en.wikipedia.org/wiki/Recommender_system

5. Keywords：recommender systems、 association rules 、collaborative filtering、slope one、 SVD、KNN....

7. Slope one

8. SVD

10. Slope one

11. SVD

14. Confidence:

15. Algorithms：Apriori algorithm、FP-growth algorithm

16. http://en.wikipedia.org/wiki/Association_rule_learning

17. Demo：Python + Orangehttp://www.fuchaoqun.com/2008/08/data-mining-with-python-orange-association_rule/

19. Slope one

20. SVD

23. http://www.daniel-lemire.com/fr/abstracts/SDM2005.html

24. Simper Could Be Better

25. Weighted Average:

26. http://en.wikipedia.org/wiki/Slope_One

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

39. LAPCKL、BLAS (Fortran)

40. numpy、scipy (Python)

41. SVDLIBC、Meschach (C)

42. http://en.wikipedia.org/wiki/Singular_value_decomposition

43. ……

44. KNN:

45. matlab

46. FLANN

47. ……

48. All in one solution:

49. DIVISI

52. Data Cleaning

53. Data Preprocessing

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

67. Store user's click

68. Data Mining