AMLD2021 - ML in online marketplaces

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This document discusses the use of machine learning in online marketplaces. It outlines how machine learning is used for recommendations, search, trust and safety, seller experience, and pricing/monetization. Specific applications mentioned include collaborative and content-based recommendation systems, ranking models for search, automated content moderation, image quality assessment, dynamic pricing, and promoting listings. The document provides examples of algorithms like counting, collaborative filtering, learning to rank, and neural networks that power these machine learning applications in online marketplaces.

Engineering

Machine Learning in
Online Marketplaces
Alexey Grigorev
14.09.2021

DataTalks.Club — @Al_Grigor
Plan
● Marketplace
● Examples of marketplaces
● Use cases of ML in marketplaces
○ Search
○ Recommendation
○ Trust and Safety
○ Seller experience
○ Pricing and monetization
○ Other examples
● Conclusion

DataTalks.Club — @Al_Grigor
Source: https://www.dvnovosti.ru/eao/2019/08/24/103683/

DataTalks.Club — @Al_Grigor
Seller
Icons: Bananas, Apple

DataTalks.Club — @Al_Grigor
Buyer Seller
Icons: Bananas, Apple

DataTalks.Club — @Al_Grigor
Buyer Seller
Icons: Bananas, Apple
Marketplace

DataTalks.Club — @Al_Grigor
Buyer Seller
Online
Marketplace
Icons: Bananas, Apple
Website
💳

DataTalks.Club — @Al_Grigor
Buyer
(Demand side)
Seller
(Supply side)
Two-sided
Marketplace
Website

DataTalks.Club — @Al_Grigor
ML in online marketplaces
● Recommendations
● Search
● Trust and safety
● Seller experience
● Pricing and monetization
● Other examples

DataTalks.Club — @Al_Grigor
Amazing 5G phone
With 100500 GB RAM
10 cameras
Unlimited disk space
Customers who bought this also bought ...
Icons: srip

DataTalks.Club — @Al_Grigor
Algorithms
● Counting
● Collaborative filtering
● Neural networks

DataTalks.Club — @Al_Grigor
Counting
SELECT country, item, count(1)
FROM views
GROUP BY country, item

DataTalks.Club — @Al_Grigor
Collaborative filtering
A
Users
Items
≈ U V
x

DataTalks.Club — @Al_Grigor
Collaborative filtering
Users
Items
≈ x
Item
vector
User
vector

DataTalks.Club — @Al_Grigor
Collaborative filtering
Icons: srip
similarity

DataTalks.Club — @Al_Grigor
Neural networks (“item2vec”)
Icons: srip
Neural network
Neural network
similarity

DataTalks.Club — @Al_Grigor
https://habr.com/ru/company/avito/blog/491942/
Deep Learning Recommender Systems - Cristian Martinez, Ilia Ivanov (video)

DataTalks.Club — @Al_Grigor
Cool devices
Icons: srip
🔍

DataTalks.Club — @Al_Grigor
Algorithms
● Bag of words and TF-IDF
● Learning to rank

DataTalks.Club — @Al_Grigor
Amazing 5G phone
With 100500 GB RAM
10 cameras
Unlimited disk space
Icons: srip
Apache
Lucene
Index

DataTalks.Club — @Al_Grigor
Amazing 5G phone
With 100500 GB RAM
10 cameras
Unlimited disk space
Icons: srip
Apache
Lucene
Cool devices
Index
Query

$DataTalks.Club — @Al_Grigor TF-IDF Q: {"cool": 1.1, "device": 1.2} D1: {"apple": 1.2, "5g": 1.0, "hip": 1.5} D2: {"samsung": 1.1, "joystick": 1.7, ...} D3: {"hp": 2.2, "laptop": 1.2, "used": 0.6, ...} D4: {"deep": 0.9, "learning": 1.0, "printer": 2.3, ...}$

DataTalks.Club — @Al_Grigor
doc
query
θ
angle between vectors
w2
w1

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Learning to rank
Query 1 Document 1 Yes
Query 1 Document 2 No
Query 1 Document 3 No
Query 2 Document 1 No
Query 2 Document 2 No
Query 2 Document 3 Yes

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Query (user)
Document
Model P(relevant)

DataTalks.Club — @Al_Grigor
Query
Document
Neural network
Neural network
similarity

DataTalks.Club — @Al_Grigor
User generated content
Such description. So much text

DataTalks.Club — @Al_Grigor
User generated content
Such description. So
much text
Such description. So
much text
Such description. So
much text

DataTalks.Club — @Al_Grigor
User generated content

DataTalks.Club — @Al_Grigor
Problems:
● Illegal content
● NSFW content
● Duplicates
● Spam
● Fraud

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Content moderation
Such description
So much text

DataTalks.Club — @Al_Grigor
Content moderation
ML
Such description
So much text
Automatic
moderation system

DataTalks.Club — @Al_Grigor
Content moderation
ML
Such description
So much text
Accept
Reject
Moderation queue
MP
Automatic
moderation system
Moderation panel
Accept
Reject
Moderators

DataTalks.Club — @Al_Grigor
ML
Automatic
moderation system
Duplicates
Forbidden
items
NSFW
Spam Fraud

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category:
Truck
category:
Fridge
category:
Machete
Forbidden items

DataTalks.Club — @Al_Grigor
https://tech.olx.com/a-two-step-framework-for-duplicate-detection-fbbe4c905480
https://tech.olx.com/detecting-image-duplicates-at-olx-scale-7f59e4b6aef4

DataTalks.Club — @Al_Grigor
I want to
sell my
car
Picture by John Torcasio from unsplash (source)

DataTalks.Club — @Al_Grigor
Picture by John Torcasio from unsplash (source)
��

DataTalks.Club — @Al_Grigor
🚗 Model Price

DataTalks.Club — @Al_Grigor
quality:
Good
quality:
Bad
Image quality

DataTalks.Club — @Al_Grigor
AI in online marketplaces
● Recommendations
● Search
● Trust and safety
● Seller experience
● Pricing and monetization
● Other examples

DataTalks.Club — @Al_Grigor
��
🚗
Icons: Concert

DataTalks.Club — @Al_Grigor
��
🚗
Icons: Concert
��
��
��
��
🚗
🚗
“Surge”

DataTalks.Club — @Al_Grigor
Icons: srip
Cool devices
🚀
Awesome
Promoted
Ad
🔍

DataTalks.Club — @Al_Grigor
Plan
● Marketplace
● Examples of marketplaces
● Use cases of ML in marketplace
○ Search
○ Recommendation
○ Trust and Safety
○ Seller experience
○ Pricing and monetization
○ Other examples
● Conclusion

DataTalks.Club — @Al_Grigor
Conclusions
● ML in marketplaces is everywhere
● ML helps buyers: search, recommendation, trust and safety
● ML helps sellers: seller experience
● ML helps make money: pricing and monetization

DataTalks.Club — @Al_Grigor
@Al_Grigor
agrigorev
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The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been referred to as the "New Great Game." This research centres on the power struggle, considering geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil politics, and conventional and nontraditional security are all explored and explained by the researcher. Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role in Central Asia. This study adheres to the empirical epistemological method and has taken care of objectivity. This study analyze primary and secondary research documents critically to elaborate role of china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade, pipeline politics, and winning states, according to this study, thanks to important instruments like the Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study, China is seeing significant success in commerce, pipeline politics, and gaining influence on other governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai Cooperation Organisation and the Belt and Road Economic Initiative.

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AMLD2021 - ML in online marketplaces

1. Machine Learning in Online Marketplaces Alexey Grigorev 14.09.2021

2. Hello 👋 I’m Alexey.

3. DataTalks.Club — @Al_Grigor Plan ● Marketplace ● Examples of marketplaces ● Use cases of ML in marketplaces ○ Search ○ Recommendation ○ Trust and Safety ○ Seller experience ○ Pricing and monetization ○ Other examples ● Conclusion

4. DataTalks.Club — @Al_Grigor Source: https://www.dvnovosti.ru/eao/2019/08/24/103683/

5. DataTalks.Club — @Al_Grigor Seller

6. DataTalks.Club — @Al_Grigor Seller Icons: Bananas, Apple

7. DataTalks.Club — @Al_Grigor Buyer Seller Icons: Bananas, Apple

8. DataTalks.Club — @Al_Grigor Buyer Seller Icons: Bananas, Apple 💶 💶 💶

9. DataTalks.Club — @Al_Grigor Buyer Seller Icons: Bananas, Apple 💶 💶 💶

10. DataTalks.Club — @Al_Grigor Buyer Seller Icons: Bananas, Apple Marketplace

11. DataTalks.Club — @Al_Grigor Buyer Seller Online Marketplace Icons: Bananas, Apple Website 💳

12. DataTalks.Club — @Al_Grigor Buyer (Demand side) Seller (Supply side) Two-sided Marketplace Website

13.

14. DataTalks.Club — @Al_Grigor

15. DataTalks.Club — @Al_Grigor

16. DataTalks.Club — @Al_Grigor

17. DataTalks.Club — @Al_Grigor

18. DataTalks.Club — @Al_Grigor

19. DataTalks.Club — @Al_Grigor ML in online marketplaces ● Recommendations ● Search ● Trust and safety ● Seller experience ● Pricing and monetization ● Other examples

20. DataTalks.Club — @Al_Grigor

21. DataTalks.Club — @Al_Grigor Icons: srip

22. DataTalks.Club — @Al_Grigor Amazing 5G phone With 100500 GB RAM 10 cameras Unlimited disk space Customers who bought this also bought ... Icons: srip

23. DataTalks.Club — @Al_Grigor Algorithms ● Counting ● Collaborative filtering ● Neural networks

24. DataTalks.Club — @Al_Grigor Icons: srip

25. DataTalks.Club — @Al_Grigor Counting SELECT country, item, count(1) FROM views GROUP BY country, item

26. DataTalks.Club — @Al_Grigor Collaborative filtering A Users Items ≈ U V x

27. DataTalks.Club — @Al_Grigor Collaborative filtering Users Items ≈ x Item vector User vector

28. DataTalks.Club — @Al_Grigor d2 d1 󰬋 🧦 🧸

29. DataTalks.Club — @Al_Grigor d2 d1 󰬋 🧦 🧸

30. DataTalks.Club — @Al_Grigor Amazing 5G phone With 100500 GB RAM 10 cameras Unlimited disk space Customers who bought this also bought ... Icons: srip

31. DataTalks.Club — @Al_Grigor Collaborative filtering Icons: srip similarity

32. DataTalks.Club — @Al_Grigor Neural networks (“item2vec”) Icons: srip Neural network Neural network similarity

33. DataTalks.Club — @Al_Grigor https://habr.com/ru/company/avito/blog/491942/ Deep Learning Recommender Systems - Cristian Martinez, Ilia Ivanov (video)

34. DataTalks.Club — @Al_Grigor ML in online marketplaces ● Recommendations ● Search ● Trust and safety ● Seller experience ● Pricing and monetization ● Other examples

35. DataTalks.Club — @Al_Grigor Cool devices Icons: srip 🔍

36. DataTalks.Club — @Al_Grigor Algorithms ● Bag of words and TF-IDF ● Learning to rank

37. DataTalks.Club — @Al_Grigor Amazing 5G phone With 100500 GB RAM 10 cameras Unlimited disk space Icons: srip Apache Lucene Index

38. DataTalks.Club — @Al_Grigor Amazing 5G phone With 100500 GB RAM 10 cameras Unlimited disk space Icons: srip Apache Lucene Cool devices Index Query

39. DataTalks.Club — @Al_Grigor TF-IDF Q: {"cool": 1.1, "device": 1.2} D1: {"apple": 1.2, "5g": 1.0, "hip": 1.5} D2: {"samsung": 1.1, "joystick": 1.7, ...} D3: {"hp": 2.2, "laptop": 1.2, "used": 0.6, ...} D4: {"deep": 0.9, "learning": 1.0, "printer": 2.3, ...}

40. DataTalks.Club — @Al_Grigor doc query θ angle between vectors w2 w1

41. DataTalks.Club — @Al_Grigor Learning to rank Query 1 Document 1 Yes Query 1 Document 2 No Query 1 Document 3 No Query 2 Document 1 No Query 2 Document 2 No Query 2 Document 3 Yes

42. DataTalks.Club — @Al_Grigor Query (user) Document Model P(relevant)

43. DataTalks.Club — @Al_Grigor Query Document Neural network Neural network similarity

44. DataTalks.Club — @Al_Grigor ML in online marketplaces ● Recommendations ● Search ● Trust and safety ● Seller experience ● Pricing and monetization ● Other examples

45. DataTalks.Club — @Al_Grigor User generated content Such description. So much text

46. DataTalks.Club — @Al_Grigor User generated content Such description. So much text Such description. So much text Such description. So much text

47. DataTalks.Club — @Al_Grigor User generated content

48. DataTalks.Club — @Al_Grigor Problems: ● Illegal content ● NSFW content ● Duplicates ● Spam ● Fraud

49. DataTalks.Club — @Al_Grigor Content moderation Such description So much text

50. DataTalks.Club — @Al_Grigor Content moderation ML Such description So much text Automatic moderation system

51. DataTalks.Club — @Al_Grigor Content moderation ML Such description So much text Accept Reject Moderation queue MP Automatic moderation system Moderation panel Accept Reject Moderators

52. DataTalks.Club — @Al_Grigor ML Automatic moderation system Duplicates Forbidden items NSFW Spam Fraud

53. DataTalks.Club — @Al_Grigor category: Truck category: Fridge category: Machete Forbidden items

54. DataTalks.Club — @Al_Grigor https://tech.olx.com/a-two-step-framework-for-duplicate-detection-fbbe4c905480 https://tech.olx.com/detecting-image-duplicates-at-olx-scale-7f59e4b6aef4

55. DataTalks.Club — @Al_Grigor ML in online marketplaces ● Recommendations ● Search ● Trust and safety ● Seller experience ● Pricing and monetization ● Other examples

56. DataTalks.Club — @Al_Grigor I want to sell my car Picture by John Torcasio from unsplash (source)

57. DataTalks.Club — @Al_Grigor Picture by John Torcasio from unsplash (source) ��

58. DataTalks.Club — @Al_Grigor ��

59. DataTalks.Club — @Al_Grigor ��

60. DataTalks.Club — @Al_Grigor 🚗 Model Price

61. DataTalks.Club — @Al_Grigor �� 🥳 50000

62. DataTalks.Club — @Al_Grigor quality: Good quality: Bad Image quality

63. DataTalks.Club — @Al_Grigor AI in online marketplaces ● Recommendations ● Search ● Trust and safety ● Seller experience ● Pricing and monetization ● Other examples

64. DataTalks.Club — @Al_Grigor ��

65. DataTalks.Club — @Al_Grigor �� 🚗

66. DataTalks.Club — @Al_Grigor �� 🚗 Icons: Concert

67. DataTalks.Club — @Al_Grigor �� 🚗 Icons: Concert �� 🚗 🚗 “Surge”

68. DataTalks.Club — @Al_Grigor Icons: srip Cool devices 🚀 Awesome Promoted Ad 🔍

69. DataTalks.Club — @Al_Grigor Plan ● Marketplace ● Examples of marketplaces ● Use cases of ML in marketplace ○ Search ○ Recommendation ○ Trust and Safety ○ Seller experience ○ Pricing and monetization ○ Other examples ● Conclusion

70. DataTalks.Club — @Al_Grigor Conclusions ● ML in marketplaces is everywhere ● ML helps buyers: search, recommendation, trust and safety ● ML helps sellers: seller experience ● ML helps make money: pricing and monetization

71. DataTalks.Club — @Al_Grigor @Al_Grigor agrigorev DataTalks.Club

AMLD2021 - ML in online marketplaces

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