The slides from my talk from DataFest 2020

1. “data science”
“counting”
“machine learning”
Alexey Grigorev
+
−

2. Hello 👋
I’m Alexey.
I like counting
Icon: Freepic

4. Plan
● Process
● Examples
○ Recommendations
○ Advertisement
○ Duplicate detection
○ Fraud detection
○ Search

5. Picture: CRISP-DM

6. Picture: CRISP-DM

7. Picture: CRISP-DM

8. Plan
● Process
● Examples
○ Recommendations
○ Advertisement
○ Duplicate detection
○ Fraud detection
○ Search

10. Icons: srip

11. Recommendations
Show the most popular items!
SELECT item, count(1)
FROM views
GROUP BY item

12. Recommendations
SELECT country, item, count(1)
FROM views
GROUP BY country, item

13. Metric
Time

14. Amazing 5G phone
With 100500 GB RAM
10 cameras
Unlimited disk space

15. Amazing 5G phone
With 100500 GB RAM
10 cameras
Unlimited disk space
Customers who bought this also bought ...

16. Recommendations
SELECT item1, item2, count(1)
FROM item_pairs
GROUP BY item1;

17. Metric
Time

18. Metric
Time
Now add ML!

19. Metric
Time
Now add ML! Or...

20. Plan
● Process
● Examples
○ Recommendations
○ Advertisement
○ Duplicate detection
○ Fraud detection
○ Search

23. Exchange

24. Exchange
DSP1
DSP2
DSP3
...

25. Exchange
DSP1
DSP2
DSP3
...

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

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

28. Exchange
$0.10

29. Exchange
$0.10

30. Exchange
{
"app": {
"cat": ["IAB14"],
"id": "10000001", "name": "APP_NAME",
"publisher": { "id": "100000001", "name": "COMPANY" }
},
"device": {
"connectiontype": 0, "devicetype": 1,
"ifa": "e4273e31-97a9-4b29-93a8-8a99f0cea068",
"geo": {
"country": "USA", "lat": 29.8327, "lon": -95.6627,
"type": 1, "zip": "77084"
},
"ip": "172.56.14.6",
"make": "Generic", "model": "Windows Phone 8",
"os": "Windows Phone OS", "osv": "8"
},
"user": {
"gender": "M", "yob": 1976
}
}
https://wiki.smaato.com/pages/viewpage.action?pageId=3670020

31. Exchange
{
"app": {
"cat": ["IAB14"],
"id": "10000001", "name": "APP_NAME",
"publisher": { "id": "100000001", "name": "COMPANY" }
},
"device": {
"connectiontype": 0, "devicetype": 1,
"ifa": "e4273e31-97a9-4b29-93a8-8a99f0cea068",
"geo": {
"country": "USA", "lat": 29.8327, "lon": -95.6627,
"type": 1, "zip": "77084"
},
"ip": "172.56.14.6",
"make": "Generic", "model": "Windows Phone 8",
"os": "Windows Phone OS", "osv": "8"
},
"user": {
"gender": "M", "yob": 1976
}
}
Category
App name
Device ID

32. Exchange
DSP1
DSP2
DSP3
...
Everyone gets this information!

33. User profiles
SELECT device_id, category, count(1)
FROM bid_requests
GROUP BY device_id, category;

34. User profiles
● Adventure games: 100 visits
● Casual games: 1000 visits
● Business: 1 visit

35. Plan
● Process
● Examples
○ Recommendations
○ Advertisement
○ Duplicate detection
○ Fraud detection
○ Search

36. Icons: srip

37. Image hashes
94088af86c038327
14ee7fe587860078a1109033318bd986
94088af86c038327
07aaedb9b75e88a6051184f01be5cc50

38. SELECT phash, count(1)
FROM image_hashes
GROUP BY phash
HAVING count(1) > 1;

39. Approximate duplicates
94088af86c038327
9408 | 8af8 | 6c03 | 8327
Count number of matched chunks
Use hamming distance

40. Hashes
● Images: perceptual hashes
● Texts: minhash, simhash
● Vectors: random projections

41. Plan
● Process
● Examples
○ Recommendations
○ Advertisement
○ Duplicate detection
○ Fraud detection
○ Search

42. Amazing 5G phone, $100
With 100500 GB RAM
10 cameras
Unlimited disk space
Icons: srip, freepik
report

43. Seller
● User ID
● IP address
● Signature

44. Seller
● User ID
● IP address
● Signature
report
count ← count + 1
count ← count + 1
count ← count + 1

45. If count(IP_address) > 10:
Investigate

46. Other examples
● Count messages (also hashed)
● Irregular activity
● Many others

47. Plan
● Process
● Examples
○ Recommendations
○ Advertisement
○ Duplicate detection
○ Fraud detection
○ Search

50. Q: Information
D1: Counting is not hip
D2: Information extraction is the process of ...
D3: Information gain is used to …
D4: Deep learning is better than counting

51. Q: {"information": 1}
D1: {"counting": 1, "not": 1, "hip": 1}
D2: {"information": 1, "extraction": 1, "process": 1, ...}
D3: {"information": 2, "gain": 1, "used": 1, ...}
D4: {"deep": 1, "learning": 1, "better": 1, "counting": 1}

52. Q: {"information": 1}
D1: {"counting": 1, "not": 1, "hip": 1}
D2: {"information": 1, "extraction": 1, "process": 1, ...}
D3: {"information": 2, "gain": 1, "used": 1, ...}
D4: {"deep": 1, "learning": 1, "better": 1, "counting": 1}

53. Q: {"information": 1}
D3: {"information": 2, "gain": 1, "used": 1, ...}
D2: {"information": 1, "extraction": 1, "process": 1, ...}

54. TF-IDF
Q: {"information": 1.1}
D1: {"counting": 1.2, "not": 0.2, "hip": 1.5}
D2: {"information": 1.1, "extraction": 1.7, ...}
D3: {"information": 2.2, "gain": 1.2, "used": 0.6, ...}
D4: {"deep": 0.9, "learning": 1.0, "better": 0.3, ...}

55. Cosine similarity
doc
query
θ
angle between vectors
w2
w1

56. Cosine similarity
doc
query
θ
angle between vectors
w2
w1

57. Cosine similarity + TF-IDF
We can use it for:
● Search
● Recommendations
● User profiling
● Near-duplicate detection

59. Plan
● Process
● Examples
○ Recommendations
○ Advertisement
○ Duplicate detection
○ Fraud detection
○ Search

60. Key takeaway
● It’s okay to start without machine learning
● First, do heuristics (counts)
● Prove that it’s useful
● Iterate

61. mlbookcamp.com
● When you already have counting
● Time to add some machine learning!
● http://bit.ly/mlbookcamp
● Get 40% off with code “grigorevpc”
Machine Learning
Bookcamp

62. @Al_Grigoragrigorev
alexeygrigorev.com
mlbookcamp.com
Get the slides and
win a free copy of
ML Bookcamp!
https://airtable.com/shrU3cwHBcoAaIKqM
QR