Palestrante convidado do 1o Workshop de Inteligência Artificial de Mato Grosso do Sul, contendo temas de machine learning, data mining e visualization

1. Robson Motta | robson@chaordic.com.br
7 Machine Learning techniques
in practice in a Startup

2. 312.000.000.000
(this means billions)
recommendations
in 2014

3. Get to know
our solutions

4. How to present the
best
recommendation
for each client/context?

5. recommendations
data

6. recommendations
data
preprocessing
processing
postprocessing
● products
● pageviews
● clicks
● buyorders
etc.

7. Machine Learning
“All models are wrong,
but some are useful”
(George E. P. Box)

8. … first ecommerce
We have
a client!

10. Content-based
Filtering
1

11. Content-based
Filtering
1
frequency of term
n in document d
IDF factor of
term n
weight of term n
within document d

12. Content-based
Filtering
1
reference
reference
reference
reference

13. Content-based
Filtering
1

14. Content-based
Filtering
1

16. Content-based
Filtering
1

17. Content-based
Filtering
1
Clustering
2

18. Clustering
2

19. Clustering
2

20. Clustering
2
iteration 1

21. Clustering
2
iteration 2

22. Clustering
2
iteration 3

23. Clustering
2
iteration 4

24. Clustering
2
iteration 10

25. Clustering
2
iteration 12

26. Clustering
2

27. … main issues
the number
of clusters
Clustering
2

28. Clustering
2
iteration 12

29. Clustering
2
iteration 13

30. … main issues
false positives
(pair of products wrongly
assigned to the same cluster)
false negatives
(pair of products wrongly
assigned to different clusters)
Clustering
2

31. Clustering
2
iteration 12

32. Clustering
2
iteration 12

33. … what did we learn?
clustering algorithms
+
evaluation metrics
Clustering
2

34. … second ecommerce
We have
another client!
Clustering
2

35. … second ecommerce
We have
another client!
And this one has categories!
Clustering
2

36. Clustering
2
Classification
3

37. Classification
3

38. Classification
3

39. Classification
3

40. Classification
3

41. … what did we learn?
committee
(modified SVM + kNN)
Clustering
2

42. … main issues
unbalanced classes
unlabeled areas
Classification
3

43. Classification
3
Active
Learning
3

44. Active Learning
4

45. Active Learning
4

46. Active Learning
4

47. Active Learning
4

48. Active Learning
4

49. Active Learning
4

50. Active Learning
4

51. … what did we learn?
distance:
new areas
confidence:
fix incorrectly classified
Active Learning
4

52. Active Learning
4
5
Community extraction
(networks)

53. Community
extraction
(networks)
5

54. Community
extraction
(networks)
5

55. Community
extraction
(networks)
5

56. … what did we learn?
committee
(clustering + community extraction)
Community
extraction
(networks)
5

57. Community
extraction
(networks)
5
Collaborative
Filtering
6

58. Collaborative
Filtering
6
Customers Who Bought This Item Also Bought, PaulsHealthBlog.com, 11.04.2014

59. Collaborative
Filtering
6

60. Collaborative
Filtering
6

61. Collaborative
Filtering
6

62. Collaborative
Filtering
6

63. Collaborative
Filtering
6

64. Collaborative
Filtering
6

65. Collaborative
Filtering
6

66. Collaborative
Filtering
6

67. Collaborative
Filtering
6
user-based

68. Collaborative
Filtering
6
10 5 7 0 2 3 4 1
...

69. Collaborative
Filtering
6
10 5 7 0 2 3 4 1
...
item-based

70. Collaborative
Filtering
6
Multi-armed
Bandit
7

71. Multi-armed
Bandit
7
Exploration-Exploitation
trade-off

72. Multi-armed
Bandit
7
… case 1
algorithm 2
algorithm 1
…
algorithm N

73. Multi-armed
Bandit
7
… case 2
order 2
order 1
…

74. Multi-armed
Bandit
7 chance to be picked

75. Multi-armed
Bandit
7 chance to be picked

76. Multi-armed
Bandit
7 chance to be picked

77. Multi-armed
Bandit
7 chance to be picked

78. Multi-armed
Bandit
7 chance to be picked
user feedback: click

79. Multi-armed
Bandit
7 chance to be picked

80. Multi-armed
Bandit
7 chance to be picked
user feedback: click

81. recommendations
data
preprocessing
mining
postprocessing
● products
● pageviews
● clicks
● buyorders
etc.

82. Challenges
+
...
popular items
outliers
incompatible
principal-accessory
+
+

83. How do we
guarantee quality
to our clients?
● subjective evaluation: visualization
● objective evaluation: quality measures
● online evaluation: A/B test and Bandit

84. Multidimensional Projection
(tSNE technique)

85. Stability, purity and coverage measures

86. Circular connected chart

87. A/B tests

88. Robson Motta
robson@chaordic.com.br