Upcoming SlideShare
-
Full NameComment goes here.Delete Reply Block
-
Jessica AdamsIf you’re struggling with your assignments like me, check out ⇒ www.HelpWriting.net ⇐. My friend sent me a link to to tis site. This awesome company. After I was continuously complaining to my family and friends about the ordeals of student life. They wrote my entire research paper for me, and it turned out brilliantly. I highly recommend this service to anyone in my shoes. ⇒ www.HelpWriting.net ⇐.Reply -
Brooke RatliffJust recently I emailed my friend and said "Funny (well, not really) how many psychologists, doctors, dieticians, support groups, hospitals, and even a hypnotist, I went to over the years that couldn’t help me – and all it took in the end was a simple site like this, and a supportive partner! I guess everyone is different though... ★★★ http://t.cn/A6Pq6OB6Reply
-
KalieTomiesGirls for sex in your area are there: tinyurl.com/areahotsexReply
-
KalieMerisSex in your area is here: www.bit.ly/sexinareaReply
-
KalieMerisDating for everyone is here: www.bit.ly/2AJerkHReply
Show More
AWS를 활용한 상품 추천 서비스 구축::김태현:: AWS Summit Seoul 2018
- 1. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. 김태현 Solutions Architect / Amazon Web Services AWS를 활용한 상품 추천 서비스 구축
- 2. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. 추천이란? 추천 알고리즘 유사도 알고리즘 추천 시스템 아키텍쳐 성능평가
- 3. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. About Me
- 4. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. 추천이란?
- 5. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Amazon - 상품 추천
- 6. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Netflix - 영화 추천
- 7. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. 추천 서비스는 왜 필요할까?
- 8. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. • 아마존 전체 매출의 35%는 추천에서 발생 • 넷플릭스의 75% 사용자가 추천을 통해 영화를 선택 https://www.mckinsey.com/industries/retail/our- insights/how-retailers-can-keep-up-with-consumers
- 9. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Amazon을 좀 더 살펴보면
- 10. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Amazon - Home
- 11. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Amazon - Product
- 12. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Amazon - Product
- 13. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Amazon - Product
- 14. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. 추천 알고리즘
- 15. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. 추천 알고리즘 • CF (Collaborative Filtering) • User-based • Item-based • CBF (Contents Based Filtering) • Text • Image • AR (Association Rule)
- 16. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. CF (Collaborative Filtering)
- 17. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. User-based filtering
- 18. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Item-based filtering
- 19. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Item-based filtering
- 20. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. CF를 구현하는 방법 1 2 3 4 2 4 5
- 21. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. CF를 구현하는 방법 1 2 3 4 5 1 1 1 1 1 1 10 0 0
- 22. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. CF를 구현하는 방법 1 2 3 4 5
- 23. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. 규모(Scalability)의 문제 ?
- 24. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Pre-Clustering
- 25. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. MinHash Hash Function e883ba0a24d01f
- 26. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. MinHash Hash Function 1 Hash Function 2 1 0 0 1 1 0 1 1 1 0
- 27. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Pre-Clustering
- 28. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Pre-Clustering Calculation Time Cluster Size
- 29. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. CF 단점 • Cold Start • 신규 상품 추천 X • 사용자가 보지않는 상품 추천 X • 해결책 • CBF (Contents Based Filtering)
- 30. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. CBF (Contents Based Filtering)
- 31. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. CBF (Contents Based Filtering) • Contents • Text • Image
- 32. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. CBF – Word2Vec for Text
- 33. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. CBF – Word2Vec for Text
- 34. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. CBF – Deep Learning for Image
- 35. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. CBF – Deep Learning for Image
- 36. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Hybrid
- 37. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Hybrid • CF (Collaborative Filtering) • 효과 , 커버리지 • CBF (Contents Based Filtering) • 커버리지 • CF + CBF • Main 알고리즘은 CF • CF 추천 결과가 모자란 경우 CBF로 보완 • 패션 혹은 가구와 같은 특정 카테고리의 경우 CBF 효과가 좋을 수 있음
- 38. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. AR (Association Rule)
- 39. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. AR (Association Rule)
- 40. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. AR 단점 • 커버리지 • 정확하게 같이 구매한 상품만을 대상으로 하기 때문 • 해결책 • AR(정확도 ) + CF(커버리지 ) • 구매로그 기반의 CF를 사용하면 AR과 같은 효과를 얻을 수 있음.
- 41. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. 자주 함께 구매하는 상품
- 42. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. 유사도 알고리즘
- 43. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. 유사도 알고리즘 • Jaccard • Cosine • ETC • Euclidean • Manhattan • Pearson • Tanimoto
- 44. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Jaccard
- 45. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. Cosine X Y Z A B Cosine
- 46. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. 추천 시스템 아키텍쳐
- 47. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. 추천 시스템 아키텍쳐 EMRS3 DynamoDB ElastiCache Glue LambdaAPI Gateway Kinesis Firehose
- 48. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. 최근 본 상품
- 49. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. 추천 시스템 아키텍쳐 ElastiCache LambdaAPI Gateway
- 50. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. 최근 본 상품 Key Items Score User1 Item1 20180101000000 item2 20180201000000 item3 20180301000000 User2 item4 20180101000000 item5 20180201000000 item6 20180301000000 TTL 과 Max Item 관리 가능 Redis Sorted Set
- 51. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. 데이터 레이크
- 52. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. 데이터 레이크 S3 Glue LambdaAPI Gateway Kinesis Firehose
- 53. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. 데이터 레이크 • 하나의 중앙 저장소에 모든 데이터를 저장하고 분석 • 데이터 레이크는 S3에 데이터를 저장하는 것으로 시작 • Glue 데이터 카탈로그는 데이터에 대한 단일 뷰를 제공 • 데이터 레이크 성능 향상 팁 • 작은 파일 통합(512MB ~ 1GB) • 컬럼 포맷 사용(Parquet, ORC) • 압축(Snappy) • 파티션
- 54. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. 상품 추천
- 55. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. 추천 시스템 아키텍쳐 EMRS3 DynamoDB Glue LambdaAPI Gateway Kinesis Firehose
- 56. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. 상품 추천을 좀 더 쉽게
- 57. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. 추천 시스템 아키텍쳐 S3 Glue LambdaAPI Gateway Kinesis Firehose SageMaker
- 58. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. SageMaker Amazon SageMaker 데이터 과학자와 개발자들이 머신러닝 기반의 모델을 빠르고 쉽게 만들도록 해주는 완전 관리형 플랫폼 서비스
- 59. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. SageMaker Amazon SageMaker 1 2 3 4 I I I I Notebook Instances Algorithms ML Training Service ML Hosting Service
- 60. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. SageMaker Problem Algorithm Learning Typ Discrete Classification, Regression Linear Learner Supervised XGBoost Algorithm Supervised Discrete Recommendations Factorization Machines Supervised Image Classification Image Classification Algorithm Supervised, CNN Neural Machine Translation Sequence to Sequence Supervised, seq2seq Time-series Prediction DeepAR Supervised, RNN Discrete Groupings K-Means Algorithm Unsupervised Dimensionality Reduction PCA (Principal Component Analysis) Unsupervised Topic Determination Latent Dirichlet Allocation (LDA) Unsupervised Neural Topic Model (NTM) Unsupervised, Neural Network Based
- 61. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. 성능평가
- 62. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. 성능평가 • AB Test • On line • CTR (Click Through Ratio) • CVR (Conversion Ratio) • Off line • RMSE (Root Mean Squared Error) • MAB (Multi Armed Bandit)
- 63. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. 정리 • 데이터와 서비스에 대한 이해 • 머신러닝/딥러닝/통계 지식 필요 • 데이터 레이크 구축 필요 • 추천은 UI부터 추천 알고리즘까지 유기적으로 연결 • Offline과 Online 검증 및 테스트
- 64. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. 관련 참고 자료 • Amazon Kinesis • https://aws.amazon.com/ko/kinesis • Amazon S3 • https://aws.amazon.com/ko/s3 • AWS Glue • https://aws.amazon.com/ko/glue • Amazon SageMaker • https://aws.amazon.com/ko/sagemaker
- 65. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. AWS Summit 모바일 앱과 QR코드를 통해 강연 평가 및 설문 조사에 참여해 주시기 바랍니다. 내년 Summit을 만들 여러분의 소중한 의견 부탁 드립니다. #AWSSummit 해시태그로 소셜 미디어에 여러분의 행사 소감을 올려주세요. 발표 자료 및 녹화 동영상은 AWS Korea 공식 소셜 채널로 공유될 예정입니다. 여러분의 피드백을 기다립니다!
- 66. 감사합니다.