Sistemas de Recomendação motivam grande quantidade e diversidade de pesquisas, tanto na academia como no meio corporativo. Porém, ao se tratar de seu uso prático, até mesmo os melhores resultados científicos apresentam problemas que exigem otimização para o domínio específico. Nesta palestra teremos uma introdução da construção de recomendações no contexto de produtos em e-commerces, e de forma mais aprofundada veremos como executar uma otimização dos resultados utilizando outras técnicas de Aprendizado de Máquina com apoio de Visualização de Informação.

1. Robson Motta | robson@chaordic.com.br
Aprendizado de Máquina e Visualização de Informação
para otimização de Sistemas de Recomendação

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)

9. Collaborative
Filtering
1

10. Collaborative
Filtering
1
Customers Who Bought This Item Also Bought, PaulsHealthBlog.com, 11.04.2014

11. Collaborative
Filtering
1

12. Collaborative
Filtering
1

13. Collaborative
Filtering
1

14. Collaborative
Filtering
1

15. Collaborative
Filtering
1

16. Collaborative
Filtering
1

17. Collaborative
Filtering
1

18. Collaborative
Filtering
1

19. user-based
Collaborative
Filtering
1

20. 10 5 7 0 2 3 4 1
...
Collaborative
Filtering
1

21. 10 5 7 0 2 3 4 1
...
item-based
Collaborative
Filtering
1

22. Challenges
+...
popular items
outliers
incompatible
principal-accessory
+
+
???
new items

23. How do we
guarantee quality
to our clients?
● subjective evaluation: Visualization
● objective evaluation: Quality measures
● online evaluation: A/B test
● online optimization: Bandit

24. Multidimensional Projection
(tSNE technique)

25. Stability, purity and coverage measures

26. Content-based
Filtering
2

27. Content-based
Filtering
2
frequency of term
n in document d
IDF factor of
term n
weight of term n
within document d

28. reference
reference
reference
reference
Content-based
Filtering
2

29. Content-based
Filtering
2

30. Content-based
Filtering
2

32. Clustering
3

33. Clustering
3

34. Clustering
3

35. Clustering
3

36. … main issues
the number
of clusters
Clustering
3

37. Clustering
3

38. Clustering
3

39. … main issues
false positives
(pair of products wrongly
assigned to the same cluster)
false negatives
(pair of products wrongly
assigned to different clusters)
Clustering
3

40. Clustering
3

41. Clustering
3

42. Classification
4

43. Classification
4

44. Classification
4

45. Classification
4

46. Classification
4

47. … main issues
unbalanced classes
unlabeled areas
Classification
4

48. Challenges
+...
popular items
outliers
incompatible
principal-accessory
+
+
???
new items

49. Challenges
+...
popular items
outliers
incompatible
principal-accessory
+
+
???
new items
x

50. Circular connected chart: alternatives

51. Circular connected chart: complementars

52. Tabular information

53. Circular connected chart: complementars

54. A/B tests
+16%
clicks
final result:
10 days
95% significance

55. Multi-armed
Bandit
5

56. Multi-armed
Bandit
5
Exploration-Exploitation
trade-off

57. Multi-armed
Bandit
5
… case 1
algorithm 2
algorithm 1
…
algorithm N

58. Multi-armed
Bandit
5
… case 2
order 2
order 1
…

59. Multi-armed
Bandit
chance to
be picked
5

60. Multi-armed
Bandit
5 chance to
be picked

61. Multi-armed
Bandit
5 chance to
be picked

62. Multi-armed
Bandit
5 chance to
be picked

63. Multi-armed
Bandit
user feedback: click
5 chance to
be picked

64. Multi-armed
Bandit
5 chance to
be picked

65. Multi-armed
Bandit
user feedback: click
5 chance to
be picked

66. Bandit - Beta Distribution
http://www.distributome.org/js/sim/BetaSimulation.html
0 success and
10 attempts
0 success and
0 attempts
5 success and
10 attempts

67. http://www.distributome.org/js/sim/BetaSimulation.html
0 success and
10 attempts
0 success and
0 attempts
5 success and
10 attempts
Bandit - Beta Distribution

68. http://www.distributome.org/js/sim/BetaSimulation.html
0 success and
10 attempts
0 success and
0 attempts
5 success and
10 attempts
Bandit - Beta Distribution

69. 0 success and
10 attempts
0 success and
0 attempts
5 success and
10 attempts
Bandit - Thompson Sampling
http://www.distributome.org/js/sim/BetaSimulation.html

70. Bandit - Thompson Sampling
success and attempts: [(0, 10), (0, 7), (0, 7), (0, 6), (0, 4), (0, 3), (0, 4), (0, 3), (0, 0), (0, 0), ...
success and attempts: [(1, 44), (10, 398), (0, 66), (1, 57), (2, 25), (14, 324), (0, 3), (1, 46), ...

71. Bandit - Thompson Sampling
success and attempts: [(103, 1183), (64, 1138), (48, 900), (25, 524), (56, 527), (37, 546), (11, 216), …
success and attempts: [(143, 2227), (8, 299), (119, 1706), (28, 889), (146, 1288), (86, 1646), (63, 1272) ...

72. Bandit convergence

73. A/B tests
+3,5 %
purchases
final result:
25 days
95% significance

74. Robson Motta
robson@chaordic.com.br