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Aprendizado de maquina e visualizacao de informacao para otimizacao de sistemas de recomendacao
The document discusses machine learning and information visualization for optimizing recommendation systems, citing the volume of recommendations in 2014. It covers various methodologies such as collaborative filtering, content-based filtering, and A/B testing, emphasizing the challenges and evaluation techniques involved. The findings suggest an increase in user engagement and purchases through systematic application of these techniques.
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Aprendizado de maquina e visualizacao de informacao para otimizacao de sistemas de recomendacao
- 1. Robson Motta |robson@chaordic.com.br Aprendizado de Máquina e Visualização de Informação para otimização de Sistemas de Recomendação
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- 3. Get to know oursolutions
- 4. How to presentthe best recommendation for each client/context?
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- 6. recommendations data preprocessing processing postprocessing ● products ● pageviews ●clicks ● buyorders etc.
- 7. Machine Learning “All modelsare wrong, but some are useful” (George E. P. Box)
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- 10. Collaborative Filtering 1 Customers Who BoughtThis Item Also Bought, PaulsHealthBlog.com, 11.04.2014
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- 20. 10 5 70 2 3 4 1 ... Collaborative Filtering 1
- 21. 10 5 70 2 3 4 1 ... item-based Collaborative Filtering 1
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- 23. How do we guaranteequality to our clients? ● subjective evaluation: Visualization ● objective evaluation: Quality measures ● online evaluation: A/B test ● online optimization: Bandit
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- 25. Stability, purity andcoverage measures
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- 27. Content-based Filtering 2 frequency of term nin document d IDF factor of term n weight of term n within document d
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- 36. … main issues thenumber of clusters Clustering 3
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- 39. … main issues falsepositives (pair of products wrongly assigned to the same cluster) false negatives (pair of products wrongly assigned to different clusters) Clustering 3
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- 47. … main issues unbalancedclasses unlabeled areas Classification 4
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- 50. Circular connected chart:alternatives
- 51. Circular connected chart:complementars
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- 53. Circular connected chart:complementars
- 54. A/B tests +16% clicks final result: 10days 95% significance
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- 57. Multi-armed Bandit 5 … case 1 algorithm2 algorithm 1 … algorithm N
- 58. Multi-armed Bandit 5 … case 2 order2 order 1 …
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- 63. Multi-armed Bandit user feedback: click 5chance to be picked
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- 65. Multi-armed Bandit user feedback: click 5chance to be picked
- 66. Bandit - BetaDistribution 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 10attempts 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 10attempts 0 success and 0 attempts 5 success and 10 attempts Bandit - Beta Distribution
- 69. 0 success and 10attempts 0 success and 0 attempts 5 success and 10 attempts Bandit - Thompson Sampling http://www.distributome.org/js/sim/BetaSimulation.html
- 70. Bandit - ThompsonSampling 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 - ThompsonSampling 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) ...
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- 73. A/B tests +3,5 % purchases finalresult: 25 days 95% significance
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