Prezentacja by Tomasz Trzciński, Chief Scientist at Tooploox wygłoszona na 13tym spotkaniu Data Science Warsaw Meetup.

1. Chief Scientist at Tooploox
Assistant Professor at Warsaw University of Technology
Online content
popularity prediction
Tomasz Trzciński

2. Bio
PhD in Computer Vision
Internships at Google,
Qualcomm & Telefonica
VP R&D, PlayfulVision
Guitar player & proud father

3. http://pennystocks.la/internet-in-real-time/

4. i
How many will we get?
~22 likes
per day*
* http://popularity.csail.mit.edu/

5. What makes an image popular?
2.3M images from Flickr with
#views grouped by users
[What makes an image popular? A. Khosla, A. Das Sarma and R.Hamid, WWW’14]

6. Color?
[What makes an image popular? A. Khosla, A. Das Sarma and R.Hamid, WWW’14]

7. Visual cues?
edge/gradient
histograms
convolutional neural
network responses
bag-of-words
representation
[What makes an image popular? A. Khosla, A. Das Sarma and R.Hamid, WWW’14]

8. Social network?
#
#
#
#
#
#
mean views
of the user
images
uploaded
group
memberships
contacts
tags
title length
[What makes an image popular? A. Khosla, A. Das Sarma and R.Hamid, WWW’14]

9. Results
demo: http://popularity.csail.mit.edu/
[What makes an image popular? A. Khosla, A. Das Sarma and R.Hamid, WWW’14]

10. Results
[What makes an image popular? A. Khosla, A. Das Sarma and R.Hamid, WWW’14]

11. Selfies

12. How to take a good great #selfie?
[What a deep NN thinks about your #selfie? A. Karpathy, Blog.
Let’s ask a deep convolutional neural network!

13. How to take a good great #selfie?
[What a deep NN thinks about your #selfie? A. Karpathy, Blog.

14. Results: Best selfies
[What a deep NN thinks about your #selfie? A. Karpathy, Blog.
Top-100 Top Male

15. Results: Worst
[What a deep NN thinks about your #selfie? A. Karpathy, Blog.

16. Framing your #selfie
[What a deep NN thinks about your #selfie? A. Karpathy, Blog.
Sometimes a good selfie… is a selfie without you at all

17. Let’s record a viral movie…
220 million views

18. Early patterns reflect long-term interest
[Predicting the popularity of online content. G. Szabo and B. A. Huberman, ACM’10]

19. Prediction ~ Regression
Univariate Linear (UL)
Multivariate Linear (ML)
ML + Radial Basis Function
Support Vector Regression
[Using early patterns to predict the popularity of YouTube videos. H. Pinto et al., WSDM’13]
[Predicting popularity of online videos using Support Vector Regression. T. Trzcinski and P. Rokita, sub. TCSVT’15]
Φ(x, y) = exp
✓
−
||x − y||2
2σ2
◆
∀
t<T
viewsUL(v, T) ∼ ln (views(v, t))
∀
t1,...,tn<T
viewsML(v, T) ∼ ln (views(v, t1), ..., views(v, tn))
viewsSV R(v, T) ∼ Φ (views(v), ..., views(SV ))
viewsRBF (v, T) ∼ viewsML + Φ (views(v), ..., views(random))

20. Prediction before publication
Visual cues only
Video length
Dominant color
Scene dynamics
Text (OCR)
Faces
Clutter
Thumbnails
[Predicting popularity of online videos using Support Vector Regression. T. Trzcinski and P. Rokita, sub. TCSVT’15]

21. Opening Scene
Top-20
Worst-20

22. Results
[Predicting popularity of online videos using Support Vector Regression. T. Trzcinski and P. Rokita, sub. TCSVT’15]

23. & Data Science

24. Data
Science
viewers’ interests
virality prediction
competition analysis
trends across platforms
demographics
reverse-engineer

25. WE’RE HIRING!
Data Scientists
Machine Learning, Predictive Analytics, Data Visualization, Social Networks

26. THANK YOU!
www.tooploox.com/jobs