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Amazon Personalize
(Yan So) / Data Scientist,
Agenda
Amazon Personalize
Amazon Personalize
3,700 +
2,000 +
300 + (MAU)
6,000 2019
26TB+
250M+
33 DAG, 200+ in Airflow
🌟
,
• Rule-Based
•
•
•
• Collaborative Filtering, Factorization Machine
•
• (Amazon SageMaker)
• Deep Learning
•
•
•
• Cold Start - ,
•
•
•
• Machine Learning
• ( , , , )
•
Amazon Personalize
(2020.01): https://aws.amazon.com/ko/blogs/korea/amazon-personalize-seoul-region/
Amazon Personalize
Improve the possibilities of personalizing your service!
Take the full benefits ofAmazon Personalize
Benefits
•
• Amazon.com Machine Learning
• API Endpoint
• AutoML
•
Possibilities
(Proof of Concept)


☠
☠
, &
,
?
3700 +
10,000 +
300 + (MAU)
Amazon

RDS
Amazon

DynamoDB
Amazon

S3
Client
Amazon Kinesis

Data Firehose
Amazon

S3
Amazon

S3
AWS
Lambda Amazon

EMR
...
Build Train Tune Deploy
●
● hrnn
●
● AutoML
●
●
●
● AWS SDK
● /
● Recommendation Filtering
, ,
HPO
● Datalake
● Feature En...
Dataset group
- User-Item Interaction(M), User(O), Item(O) 3 Dataset
- Recipe User, Item dataset Metadata
.
- Data importing .
Demo
Solution
[0,1,0,1]
1: Relavant Item
0: Non-Relavant Item
.
- Precision
/ 2/4 = 0.5
- Mean Reciprocal Ranks
mean(1/2 + 1/4) = 0.375
...
( )
- CTR (Click-through Rate)
- 20%
- 10%
Lesson Learned
-
- Feature Engineering
( )
- CTR
-
Lesson Learned
- UI
- =>
Amazon Personalize
• (2019.11)
•
•
• https://aws.amazon.com/ko/about-aws/whats-new/2019/11/amazon-
personalize-now-support...
Amazon Personalize
• User Personalization (2020.08)
•
• Context
• https://aws.amazon.com/blogs/machine-learning/amazon-per...
[AWS Hero 스페셜] Amazon Personalize를 통한 개인화/추천 서비스 개발 노하우 - 소성운(크로키닷컴) :: AWS Community Day Online 2020
[AWS Hero 스페셜] Amazon Personalize를 통한 개인화/추천 서비스 개발 노하우 - 소성운(크로키닷컴) :: AWS Community Day Online 2020
[AWS Hero 스페셜] Amazon Personalize를 통한 개인화/추천 서비스 개발 노하우 - 소성운(크로키닷컴) :: AWS Community Day Online 2020
[AWS Hero 스페셜] Amazon Personalize를 통한 개인화/추천 서비스 개발 노하우 - 소성운(크로키닷컴) :: AWS Community Day Online 2020
[AWS Hero 스페셜] Amazon Personalize를 통한 개인화/추천 서비스 개발 노하우 - 소성운(크로키닷컴) :: AWS Community Day Online 2020
[AWS Hero 스페셜] Amazon Personalize를 통한 개인화/추천 서비스 개발 노하우 - 소성운(크로키닷컴) :: AWS Community Day Online 2020
[AWS Hero 스페셜] Amazon Personalize를 통한 개인화/추천 서비스 개발 노하우 - 소성운(크로키닷컴) :: AWS Community Day Online 2020
[AWS Hero 스페셜] Amazon Personalize를 통한 개인화/추천 서비스 개발 노하우 - 소성운(크로키닷컴) :: AWS Community Day Online 2020
[AWS Hero 스페셜] Amazon Personalize를 통한 개인화/추천 서비스 개발 노하우 - 소성운(크로키닷컴) :: AWS Community Day Online 2020
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[AWS Hero 스페셜] Amazon Personalize를 통한 개인화/추천 서비스 개발 노하우 - 소성운(크로키닷컴) :: AWS Community Day Online 2020

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AWS Community Day Online 2020
Amazon Personalize를 통한 개인화/추천 서비스 개발 노하우

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[AWS Hero 스페셜] Amazon Personalize를 통한 개인화/추천 서비스 개발 노하우 - 소성운(크로키닷컴) :: AWS Community Day Online 2020

  1. 1. Amazon Personalize (Yan So) / Data Scientist,
  2. 2. Agenda Amazon Personalize Amazon Personalize
  3. 3. 3,700 + 2,000 + 300 + (MAU) 6,000 2019 26TB+ 250M+ 33 DAG, 200+ in Airflow
  4. 4. 🌟 ,
  5. 5. • Rule-Based • • • • Collaborative Filtering, Factorization Machine • • (Amazon SageMaker) • Deep Learning • •
  6. 6. • • Cold Start - , • • • • Machine Learning • ( , , , ) •
  7. 7. Amazon Personalize (2020.01): https://aws.amazon.com/ko/blogs/korea/amazon-personalize-seoul-region/
  8. 8. Amazon Personalize Improve the possibilities of personalizing your service! Take the full benefits ofAmazon Personalize
  9. 9. Benefits • • Amazon.com Machine Learning • API Endpoint • AutoML •
  10. 10. Possibilities (Proof of Concept) 
 ☠ ☠ , & ,
  11. 11. ? 3700 + 10,000 + 300 + (MAU)
  12. 12. Amazon
 RDS Amazon
 DynamoDB Amazon
 S3 Client Amazon Kinesis
 Data Firehose Amazon
 S3 Amazon
 S3 AWS Lambda Amazon
 EMR AWS Glue
 Catalog Amazon
 Athena Amazon
 Personalize ML Feature Engineering AB Amazon
 SageMaker
  13. 13. Build Train Tune Deploy ● ● hrnn ● ● AutoML ● ● ● ● AWS SDK ● / ● Recommendation Filtering , , HPO ● Datalake ● Feature Engineering ● S3
  14. 14. Dataset group
  15. 15. - User-Item Interaction(M), User(O), Item(O) 3 Dataset - Recipe User, Item dataset Metadata . - Data importing .
  16. 16. Demo
  17. 17. Solution
  18. 18. [0,1,0,1] 1: Relavant Item 0: Non-Relavant Item . - Precision / 2/4 = 0.5 - Mean Reciprocal Ranks mean(1/2 + 1/4) = 0.375 - normalized discounted cumulative gains (NDCG@K)(1/log(1 + 2) + 1/log(1 + 4)) / (1/log(1 + 1) + 1/log(1 + 2)) = 0.65
  19. 19. ( ) - CTR (Click-through Rate) - 20% - 10% Lesson Learned - - Feature Engineering
  20. 20. ( ) - CTR - Lesson Learned - UI - =>
  21. 21. Amazon Personalize • (2019.11) • • • https://aws.amazon.com/ko/about-aws/whats-new/2019/11/amazon- personalize-now-supports-batch-recommendations/ • (2020.06) • ? • -> -> • , • https://aws.amazon.com/ko/blogs/machine-learning/introducing- recommendation-filters-in-amazon-personalize/
  22. 22. Amazon Personalize • User Personalization (2020.08) • • Context • https://aws.amazon.com/blogs/machine-learning/amazon-personalize- can-now-create-up-to-50-better-recommendations-for-fast-changing- catalogs-of-new-products-and-fresh-content/

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