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Aprendizado de maquina e visualizacao de informacao para otimizacao de sistemas de recomendacao

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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.

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Aprendizado de maquina e visualizacao de informacao para otimizacao de sistemas de recomendacao

  1. 1. Robson Motta | robson@chaordic.com.br Aprendizado de Máquina e Visualização de Informação para otimização de Sistemas de Recomendação
  2. 2. 312.000.000.000 (this means billions) recommendations in 2014
  3. 3. Get to know our solutions
  4. 4. How to present the best recommendation for each client/context?
  5. 5. recommendations data
  6. 6. recommendations data preprocessing processing postprocessing ● products ● pageviews ● clicks ● buyorders etc.
  7. 7. Machine Learning “All models are wrong, but some are useful” (George E. P. Box)
  8. 8. Collaborative Filtering 1
  9. 9. Collaborative Filtering 1 Customers Who Bought This Item Also Bought, PaulsHealthBlog.com, 11.04.2014
  10. 10. Collaborative Filtering 1
  11. 11. Collaborative Filtering 1
  12. 12. Collaborative Filtering 1
  13. 13. Collaborative Filtering 1
  14. 14. Collaborative Filtering 1
  15. 15. Collaborative Filtering 1
  16. 16. Collaborative Filtering 1
  17. 17. Collaborative Filtering 1
  18. 18. user-based Collaborative Filtering 1
  19. 19. 10 5 7 0 2 3 4 1 ... Collaborative Filtering 1
  20. 20. 10 5 7 0 2 3 4 1 ... item-based Collaborative Filtering 1
  21. 21. Challenges +... popular items outliers incompatible principal-accessory + + ??? new items
  22. 22. How do we guarantee quality to our clients? ● subjective evaluation: Visualization ● objective evaluation: Quality measures ● online evaluation: A/B test ● online optimization: Bandit
  23. 23. Multidimensional Projection (tSNE technique)
  24. 24. Stability, purity and coverage measures
  25. 25. Content-based Filtering 2
  26. 26. Content-based Filtering 2 frequency of term n in document d IDF factor of term n weight of term n within document d
  27. 27. reference reference reference reference Content-based Filtering 2
  28. 28. Content-based Filtering 2
  29. 29. Content-based Filtering 2
  30. 30. Clustering 3
  31. 31. Clustering 3
  32. 32. Clustering 3
  33. 33. Clustering 3
  34. 34. … main issues the number of clusters Clustering 3
  35. 35. Clustering 3
  36. 36. Clustering 3
  37. 37. … 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
  38. 38. Clustering 3
  39. 39. Clustering 3
  40. 40. Classification 4
  41. 41. Classification 4
  42. 42. Classification 4
  43. 43. Classification 4
  44. 44. Classification 4
  45. 45. … main issues unbalanced classes unlabeled areas Classification 4
  46. 46. Challenges +... popular items outliers incompatible principal-accessory + + ??? new items
  47. 47. Challenges +... popular items outliers incompatible principal-accessory + + ??? new items x
  48. 48. Circular connected chart: alternatives
  49. 49. Circular connected chart: complementars
  50. 50. Tabular information
  51. 51. Circular connected chart: complementars
  52. 52. A/B tests +16% clicks final result: 10 days 95% significance
  53. 53. Multi-armed Bandit 5
  54. 54. Multi-armed Bandit 5 Exploration-Exploitation trade-off
  55. 55. Multi-armed Bandit 5 … case 1 algorithm 2 algorithm 1 … algorithm N
  56. 56. Multi-armed Bandit 5 … case 2 order 2 order 1 …
  57. 57. Multi-armed Bandit chance to be picked 5
  58. 58. Multi-armed Bandit 5 chance to be picked
  59. 59. Multi-armed Bandit 5 chance to be picked
  60. 60. Multi-armed Bandit 5 chance to be picked
  61. 61. Multi-armed Bandit user feedback: click 5 chance to be picked
  62. 62. Multi-armed Bandit 5 chance to be picked
  63. 63. Multi-armed Bandit user feedback: click 5 chance to be picked
  64. 64. 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
  65. 65. 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
  66. 66. 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
  67. 67. 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
  68. 68. 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), ...
  69. 69. 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) ...
  70. 70. Bandit convergence
  71. 71. A/B tests +3,5 % purchases final result: 25 days 95% significance
  72. 72. Robson Motta robson@chaordic.com.br

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