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

1. Amazon Personalize
(Yan So) / Data Scientist,

2. Agenda
Amazon Personalize
Amazon Personalize

3. 3,700 +
2,000 +
300 + (MAU)
6,000 2019
26TB+
250M+
33 DAG, 200+ in Airflow

4. 🌟
,

5. • Rule-Based
•
•
•
• Collaborative Filtering, Factorization Machine
•
• (Amazon SageMaker)
• Deep Learning
•
•

6. •
• Cold Start - ,
•
•
•
• Machine Learning
• ( , , , )
•

7. Amazon Personalize
(2020.01): https://aws.amazon.com/ko/blogs/korea/amazon-personalize-seoul-region/

8. Amazon Personalize
Improve the possibilities of personalizing your service!
Take the full benefits ofAmazon Personalize

9. Benefits
•
• Amazon.com Machine Learning
• API Endpoint
• AutoML
•

10. Possibilities
(Proof of Concept)
☠
☠
, &
,

12. ?
3700 +
10,000 +
300 + (MAU)

13. 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

14. Build Train Tune Deploy
●
● hrnn
●
● AutoML
●
●
●
● AWS SDK
● /
● Recommendation Filtering
, ,
HPO
● Datalake
● Feature Engineering
● S3

15. Dataset group

17. - User-Item Interaction(M), User(O), Item(O) 3 Dataset
- Recipe User, Item dataset Metadata
.
- Data importing .

18. Demo

20. Solution

24. [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

27. ( )
- CTR (Click-through Rate)
- 20%
- 10%
Lesson Learned
-
- Feature Engineering

28. ( )
- CTR
-
Lesson Learned
- UI
- =>

29. 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/

30. 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/