Two examples: advertisement and moderation in online classifieds

1. Data Science:
Use Cases and Tools
Alexey Grigorev
28/05/2020

3. mlbookcamp.com

4. Plan
● Use cases
○ Advertisement
○ Moderation in Online Classifieds
● Base skills

5. Advertisement

7. Exchange

8. Exchange

9. Exchange
DSP1
DSP2
DSP3
...

10. Exchange
DSP1
DSP2
DSP3
...

11. Exchange
DSP1
DSP2
DSP3
...
❌
$0.10
$0.09

12. Exchange
DSP1
DSP2
DSP3
...
❌
$0.10
$0.09

13. Exchange
$0.10

14. Exchange

15. DSP1
● What do we know about the user?

16. DSP1
● What do we know about the user?
● What are they interested in?

17. DSP1
● What do we know about the user?
● What are they interested in?
● What’s the probability of click and conversion?

18. DSP1
● What do we know about the user?
● What are they interested in?
● What’s the probability of click and conversion?
● How much are we willing to pay for it?
$0.10

19. DSP1
● What do we know about the user?
● What are they interested in?
● What’s the probability of click and conversion?
● How much are we willing to pay for it?
● Should we show any ad?

20. DSP1
● What do we know about the user?
● What are they interested in?
● What’s the probability of click and conversion?
● How much are we willing to pay for it?
● Should we show any ad?

21. Probability of click
Estimate the probability that the user clicks on the ad

22. Probability of click
● Device characteristics (e.g. OS)
● Geography (country, city)
● Demography (gender, age)
● History: visited pages, installed apps

23. Probability of conversion
Estimate the probability that the user will buy the product after clicking

24. Probability of conversion
● Device characteristics (e.g. OS)
● Geography (country, city)
● Demography (gender, age)
● History: visited pages, installed apps
● Features of the advertiser (how convenient the page is, etc)

25. Tools
For data scientists
● SQL (AWS Athena)
● Apache Spark
● Scikit-Learn
● Own tools (e.g. FTRL)

26. Plan
● Use cases
○ Advertisement
○ Moderation in Online Classifieds
● Base skills

27. Classified Advertisement
Such description. So much text

28. Such description. So
much text
Such description. So
much text
Such description. So
much text

30. Problems
● Illegal goods
● NSFW content
● Duplicates
● Spam
● Fraud

31. Moderation
Such description
So much text

32. ML
Such description
So much text
Automated
moderation

33. ML
Such description
So much text
Automated
moderation

34. ML
Such description
So much text
Ad queue
MP
Moderation panel
Moderators
Automated
moderation

35. ML
Such description
So much text
Ad queue
Automated
moderation
Duplicate
detection
Illegal items
detection
Other
models

36. Illegal goods
● Analyse the title and description
● Analyse the image

37. Duplicates
● How similar the listing is to other listings
● IP addresses, device signature
● How many other ads the user postes
● City, category

38. Tools
For data scientists
● SQL (AWS Athena)
● Scikit-Learn
● TensforFlow, Apache MXNet
● Flask

39. Plan
● Use cases
○ Advertisement
○ Moderation in Online Classifieds
● Base skills

40. Base skills
● SQL, data manipulation
● Git
● Python
● NumPy, Pandas, Scikit-Learn
● Training and validating models
● Microservices, Flask, Docker

41. How to learn?
● Come up with a problem
● Look for solution (tools, libraries, tutorials)
● Solve the problem
● …
● Profit

42. How to learn?
● Come up with a problem ⇐ Important! Focus on the problem
● Look for solution (tools, libraries, tutorials)
● Solve the problem
● …
● Profit

43. mlbookcamp.com
● Learn ML by doing projects
● http://bit.ly/mlbookcamp
● Get 40% off with code “grigorevpc”
● Twitter: @Al_Grigor (book give-away
this Sunday!)
Machine Learning
Bookcamp

44. That’s all!
Questions?