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Wide&Deep Learning for Recommender Systems

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keunbong kwak

TensorflowKR PR12 논문 리뷰에서 사용한 슬라이드입니다. 구글 플레이에 적용되었던 추천엔진이구요, 2017 tensorflow dev summit에서도 발표되었던 논문입니다.

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Wide&DeepLearning forRecommenderSystem (2016) Heng-Tze Cheng et al. 발표: 곽근봉
© NBT All Rights Reserved. 이논문을선정한이유 딥러닝 기반의 추천엔진을 가장 쉽게 시작할 수 있는 방법은 무엇일까?
© NBT All Rights Reserved. TensorFlow 공식 튜토리얼
© NBT All Rights Reserved. 참고 자료 Tensorflow 튜토리얼 https://www.tensorflow.org/tutorials/wide_and_deep Summit 발표 https://www.youtube.com/watch?v=NV1tkZ9Lq48 Source Code https://github.com/tensorflow/models/tree/master/official/wide_deep
© NBT All Rights Reserved. 개요 간단한아이디어로성능을개선시킨범용추천엔진 • Memorization & Generalization • Google Play 추천엔진 개선 • TensorFlow Open Source
© NBT All Rights Reserved. 사람이배우는방법 갈매기는 날 수 있다
© NBT All Rights Reserved. 사람이배우는방법 비둘기는 날 수 있다
© NBT All Rights Reserved. 사람이배우는방법 날개 달린 동물은 날 수 있다
© NBT All Rights Reserved. 사람이배우는방법
© NBT All Rights Reserved. 사람이배우는방법
© NBT All Rights Reserved. 모델설명
© NBT All Rights Reserved. 모델설명(구글플레이앱추천엔진)
© NBT All Rights Reserved. The Wide Component
© NBT All Rights Reserved. The Wide Component
© NBT All Rights Reserved. The Wide Component
© NBT All Rights Reserved. The Wide Component
© NBT All Rights Reserved. The Deep Component
© NBT All Rights Reserved. The Deep Component
© NBT All Rights Reserved. Wide VS Deep
© NBT All Rights Reserved. Wide VS Deep
© NBT All Rights Reserved. Memorization&Generalization 특징 장점 단점 Memorization (Wide) • 히스토리 데이터 • 직접 연관 데이터 • 간단함 • 주제 식별 • 학습 데이터에 있 는 것들만 학습 가 능 • 뻔한 추천 Generalization (Deep) • Dense vector embedding • 새로운 feature 조 합 탐색 • 다양성 개선 • 추가 feature engineering이 필요 없음 • Sparse & High- rank의 경우 추천 성능이 떨어짐 • 관련 없는 추천
© NBT All Rights Reserved. Memorization&Generalization
© NBT All Rights Reserved. Joint Training
© NBT All Rights Reserved. JointTraining
© NBT All Rights Reserved. Wide& Deep Modelforappsrecommendation
© NBT All Rights Reserved. Joint Training VS Ensemble Joint Training • 동시에 학습 • 각 모델의 약점 위주로 학습할 수 있음 Ensemble • 각각 따로 학습 후 prediction때 combine됨 • 각 모델을 모두 학습시켜야해서 비용이 클 수 있음
© NBT All Rights Reserved. 3주간A/B테스트를통한성능확인 결과
© NBT All Rights Reserved. 사용해보기
© NBT All Rights Reserved. TensorFlow 공식 튜토리얼
© NBT All Rights Reserved. Wide Model을 위한 준비
© NBT All Rights Reserved. Deep Model을 위한 준비
© NBT All Rights Reserved. Wide & Deep 모델 생성
© NBT All Rights Reserved. 학습 및 결과 확인
© NBT All Rights Reserved. 결론 • Wide와 Deep을 합치니깐 잘 되더라 • 실제 Google Play에 적용 • Open Source로 공개
© NBT All Rights Reserved. 감사합니다.

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Wide&Deep Learning for Recommender Systems

  • 2. © NBT All Rights Reserved. 이논문을선정한이유 딥러닝 기반의 추천엔진을 가장 쉽게 시작할 수 있는 방법은 무엇일까?
  • 3. © NBT All Rights Reserved. TensorFlow 공식 튜토리얼
  • 4. © NBT All Rights Reserved. 참고 자료 Tensorflow 튜토리얼 https://www.tensorflow.org/tutorials/wide_and_deep Summit 발표 https://www.youtube.com/watch?v=NV1tkZ9Lq48 Source Code https://github.com/tensorflow/models/tree/master/official/wide_deep
  • 5. © NBT All Rights Reserved. 개요 간단한아이디어로성능을개선시킨범용추천엔진 • Memorization & Generalization • Google Play 추천엔진 개선 • TensorFlow Open Source
  • 6. © NBT All Rights Reserved. 사람이배우는방법 갈매기는 날 수 있다
  • 7. © NBT All Rights Reserved. 사람이배우는방법 비둘기는 날 수 있다
  • 8. © NBT All Rights Reserved. 사람이배우는방법 날개 달린 동물은 날 수 있다
  • 9. © NBT All Rights Reserved. 사람이배우는방법
  • 10. © NBT All Rights Reserved. 사람이배우는방법
  • 11. © NBT All Rights Reserved. 모델설명
  • 12. © NBT All Rights Reserved. 모델설명(구글플레이앱추천엔진)
  • 13. © NBT All Rights Reserved. The Wide Component
  • 14. © NBT All Rights Reserved. The Wide Component
  • 15. © NBT All Rights Reserved. The Wide Component
  • 16. © NBT All Rights Reserved. The Wide Component
  • 17. © NBT All Rights Reserved. The Deep Component
  • 18. © NBT All Rights Reserved. The Deep Component
  • 19. © NBT All Rights Reserved. Wide VS Deep
  • 20. © NBT All Rights Reserved. Wide VS Deep
  • 21. © NBT All Rights Reserved. Memorization&Generalization 특징 장점 단점 Memorization (Wide) • 히스토리 데이터 • 직접 연관 데이터 • 간단함 • 주제 식별 • 학습 데이터에 있 는 것들만 학습 가 능 • 뻔한 추천 Generalization (Deep) • Dense vector embedding • 새로운 feature 조 합 탐색 • 다양성 개선 • 추가 feature engineering이 필요 없음 • Sparse & High- rank의 경우 추천 성능이 떨어짐 • 관련 없는 추천
  • 22. © NBT All Rights Reserved. Memorization&Generalization
  • 23. © NBT All Rights Reserved. Joint Training
  • 24. © NBT All Rights Reserved. JointTraining
  • 25. © NBT All Rights Reserved. Wide& Deep Modelforappsrecommendation
  • 26. © NBT All Rights Reserved. Joint Training VS Ensemble Joint Training • 동시에 학습 • 각 모델의 약점 위주로 학습할 수 있음 Ensemble • 각각 따로 학습 후 prediction때 combine됨 • 각 모델을 모두 학습시켜야해서 비용이 클 수 있음
  • 27. © NBT All Rights Reserved. 3주간A/B테스트를통한성능확인 결과
  • 28. © NBT All Rights Reserved. 사용해보기
  • 29. © NBT All Rights Reserved. TensorFlow 공식 튜토리얼
  • 30. © NBT All Rights Reserved. Wide Model을 위한 준비
  • 31. © NBT All Rights Reserved. Deep Model을 위한 준비
  • 32. © NBT All Rights Reserved. Wide & Deep 모델 생성
  • 33. © NBT All Rights Reserved. 학습 및 결과 확인
  • 34. © NBT All Rights Reserved. 결론 • Wide와 Deep을 합치니깐 잘 되더라 • 실제 Google Play에 적용 • Open Source로 공개
  • 35. © NBT All Rights Reserved. 감사합니다.