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TV Ads Attribution and Gaussian Processes Adrin Jalali November 16, - PowerPoint PPT Presentation

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TV Ads Attribution and Gaussian Processes Adrin Jalali November 16, 2016 1 / 27 Problem Definition Website 2 / 27 Problem Definition Website Sources of traffic TV Ads Google Ads . . . 2 / 27 Problem Definition

  1. TV Ads Attribution and Gaussian Processes Adrin Jalali November 16, 2016 1 / 27

  2. Problem Definition ▶ Website 2 / 27

  3. Problem Definition ▶ Website ▶ Sources of traffic ▶ TV Ads ▶ Google Ads ▶ . . . 2 / 27

  4. Problem Definition ▶ Website ▶ Sources of traffic ▶ TV Ads ▶ Google Ads ▶ . . . ▶ How much those campaigns influence the website’s traffic? 2 / 27

  5. Data Normalized session count for a week 3 / 27

  6. Data Normalized session count for a day 4 / 27

  7. Training Data Normalized session count for a day, after removing data around reported events 5 / 27

  8. Gaussian Processes: Extremely Short Overview 6 4 2 0 -2 -4 -6 0 2 4 6 8 10 http://mlss.tuebingen.mpg.de/2015/slides/lawrence/lawrence.pdf

  9. Gaussian Processes: Extremely Short Overview 6 4 2 0 -2 -4 -6 0 2 4 6 8 10 http://mlss.tuebingen.mpg.de/2015/slides/lawrence/lawrence.pdf

  10. Gaussian Processes: Extremely Short Overview 6 4 2 0 -2 -4 -6 0 2 4 6 8 10 http://mlss.tuebingen.mpg.de/2015/slides/lawrence/lawrence.pdf

  11. Gaussian Processes: Extremely Short Overview 6 6 4 4 2 2 0 0 -2 -2 -4 -4 -6 -6 0 2 4 6 8 10 0 2 4 6 8 10 http://mlss.tuebingen.mpg.de/2015/slides/lawrence/lawrence.pdf

  12. Gaussian Process Regression 3 2 1 y ( x ) 0 -1 -2 -3 -2 -1 0 1 2 x Figure: Examples include WiFi localization, C14 callibration curve. http://mlss.tuebingen.mpg.de/2015/slides/lawrence/lawrence.pdf

  13. Gaussian Process Regression 3 2 1 y ( x ) 0 -1 -2 -3 -2 -1 0 1 2 x Figure: Examples include WiFi localization, C14 callibration curve. http://mlss.tuebingen.mpg.de/2015/slides/lawrence/lawrence.pdf

  14. Gaussian Process Regression 3 2 1 y ( x ) 0 -1 -2 -3 -2 -1 0 1 2 x Figure: Examples include WiFi localization, C14 callibration curve. http://mlss.tuebingen.mpg.de/2015/slides/lawrence/lawrence.pdf

  15. Gaussian Process Regression 3 2 1 y ( x ) 0 -1 -2 -3 -2 -1 0 1 2 x Figure: Examples include WiFi localization, C14 callibration curve. http://mlss.tuebingen.mpg.de/2015/slides/lawrence/lawrence.pdf

  16. Gaussian Process Regression 3 2 1 y ( x ) 0 -1 -2 -3 -2 -1 0 1 2 x Figure: Examples include WiFi localization, C14 callibration curve. http://mlss.tuebingen.mpg.de/2015/slides/lawrence/lawrence.pdf

  17. Gaussian Process Regression 3 2 1 y ( x ) 0 -1 -2 -3 -2 -1 0 1 2 x Figure: Examples include WiFi localization, C14 callibration curve. http://mlss.tuebingen.mpg.de/2015/slides/lawrence/lawrence.pdf

  18. Gaussian Process Regression 3 2 1 y ( x ) 0 -1 -2 -3 -2 -1 0 1 2 x Figure: Examples include WiFi localization, C14 callibration curve. http://mlss.tuebingen.mpg.de/2015/slides/lawrence/lawrence.pdf

  19. Gaussian Process Regression 3 2 1 y ( x ) 0 -1 -2 -3 -2 -1 0 1 2 x Figure: Examples include WiFi localization, C14 callibration curve. http://mlss.tuebingen.mpg.de/2015/slides/lawrence/lawrence.pdf

  20. Gaussian Process Regression 3 2 1 y ( x ) 0 -1 -2 -3 -2 -1 0 1 2 x Figure: Examples include WiFi localization, C14 callibration curve. http://mlss.tuebingen.mpg.de/2015/slides/lawrence/lawrence.pdf

  21. Model ▶ A periodic kernel to handle periodicity ▶ A Gaussian (RBF) kernel to handle the non-periodic part of the data ▶ A white noise kernel to handle fluctuations seen in the data 19 / 27

  22. Model ▶ A Gaussian (RBF) kernel to handle the non-periodic part of the data ▶ A white noise kernel to handle fluctuations seen in the data 20 / 27

  23. Fit the model, get expected mean and variance import GPy k e r n e l = GPy . kern .RBF( input dim =1) + GPy . kern . White ( input dim =1) m = GPy . models . GPRegression ( Xtr . reshape ( − 1 ,1) , y t r . reshape ( − 1 ,1) , k e r n e l ) m. o p t i m i z e ( ) mean , var = m. p r e d i c t ( Xte . reshape ( − 1 ,1) , f u l l c o v=False , i n c l u d e l i k e l i h o o d=True ) f i g = m. p l o t ( p l o t d e n s i t y=True ) 21 / 27

  24. Fitted Model 22 / 27

  25. Significance import math import s c i p y def phi ( x ) : ret urn 0.5 + 0.5 ∗ s c i p y . s p e c i a l . e r f ( x / math . s q r t ( 2 ) ) def s c o r e ( x ) : ret urn 1 − abs ( phi ( x ) − phi( − x ) ) y s c o r e = s c o r e ( ( y − expected mean ) / e x p e c t e d s t d ) 23 / 27

  26. Result - not so good ads Observed Expected Mean Expected Variance Score Portion Is Significant TV-ad 1.11 0.98 0.03 0.55 0.11 0.89 0.98 0.03 0.42 -0.1 TV 1.3 0.99 0.03 0.94 0.24 * 1.13 0.99 0.03 0.59 0.12 1.19 0.99 0.03 0.77 0.17 TV 1.28 1 0.03 0.91 0.22 * 1.04 1 0.03 0.2 0.04 1.53 1 0.03 1 0.34 * 1.26 1.01 0.03 0.87 0.2 1.11 1.01 0.03 0.44 0.09 1.34 1.01 0.03 0.96 0.24 * 1.26 1.02 0.03 0.86 0.19 1.4 1.09 0.02 0.96 0.22 * TV 2.57 1.09 0.02 1 0.58 * 2.77 1.1 0.02 1 0.6 * TV 1.51 1.1 0.02 0.99 0.27 * 1.3 1.1 0.02 0.8 0.15 1.34 1.1 0.02 0.87 0.18 1.3 1.1 0.02 0.79 0.15 24 / 27

  27. Result - much better ads Observed Expected Mean Expected Variance Score Portion Is Significant TV-ad 0.77 0.88 0.03 0.48 -0.14 1.02 0.88 0.03 0.59 0.14 TV 1.62 0.88 0.03 1 0.46 * TV 1.47 0.88 0.03 1 0.4 * TV 1.26 0.89 0.03 0.97 0.29 * 1.19 0.89 0.03 0.92 0.26 * 1.28 0.89 0.03 0.97 0.3 * 0.91 0.89 0.03 0.11 0.03 1.13 0.89 0.03 0.82 0.21 1.15 0.9 0.03 0.86 0.22 TV 4.45 0.9 0.03 1 0.8 * TV 5.4 0.9 0.03 1 0.83 * 3.21 0.9 0.03 1 0.72 * 2.3 0.91 0.03 1 0.61 * 1.96 0.91 0.03 1 0.54 * 1.98 0.91 0.03 1 0.54 * 1.3 0.91 0.03 0.97 0.3 * 1.47 0.92 0.03 1 0.38 * 25 / 27

  28. Acknowledgments ▶ GPy: https://github.com/SheffieldML/GPy ▶ MLSS 2015: http://mlss.tuebingen.mpg.de/2015/speakers.html ▶ GPWS 2014: http://ml.dcs.shef.ac.uk/gpss/gpws14/ ▶ This talk: http://adrin.info/tv-ad-attribution-gaussian-processes.html

  29. Finished! Thank You! Questions?

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