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Fine –grained interest matching for neural news recommendation

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taeseon ryu

안녕하세요 딥러닝 논문읽기 모임입니다 오늘 업로드된 논문 리뷰 영상은 2020 ACL 에서 발표된 Fine-grained Interest Matching For Neural News Recommendation 라는 제목의 논문입니다. 오늘 소개 드릴 논문은 사용자의 흥미에 맞게 뉴스를 추천해주는 추천시스템 논문입니다. 매일 쏟아지는 뉴스의 양이 방대하여 사용자들이 자신의 흥미에 알맞는 뉴스를 찾기 어렵습니다 따라서 뉴스추천 시스템이 필요한 기술로 자리 잡고 있습니다. 오늘 소개드릴 논문은 Conventional한 방식의 Feature Engineering 방식이 아닌, HDC라는 Convolution Network를 사용하여 좀더 깊이 있게 사용자의 잠재적인 흥미 분야를 파악하고 뉴스와 후보를 좀더 다양한 레벨에서 추천하는 방식을 제안합니다. 오늘 논문의 밑바닥부터 디테일한 부분까지 , 펀디멘탈팀 김지연님이 리뷰 도와주셨습니다.

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ⓒSaebyeol Yu. Saebyeol’s PowerPoint Fine –grained Interest Matching For Neural News Recommendation 펀다멘탈팀 김지연, 김동희, 김창연, 송헌, 이근배, 이재윤
ⓒSaebyeol Yu. Saebyeol’s PowerPoint • 새로 생성되는 방대한 뉴스의 양 • 각 개인의 다양한 흥미 분야 • 기존 추천시스템의 구조적인 한계 개인 맞춤 뉴스 추천 시스템
ⓒSaebyeol Yu. Saebyeol’s PowerPoint Related Work Part 2 Conventional Methods Deep Learning Methods • User-News Feature engineering (Phelan et al 2011) • Topic category and Feature by Bayesian (Liu et al 2013 etc.) • location based news recommendation (Son et al 2013) • Distiling Semantic Feature through auto- encoder + RNN ( Okura et al) • Extract entity thorugh knowledge graph (Wu et al 2018 ) • GRU network to long0term user preference (An et al.)
ⓒSaebyeol Yu. Saebyeol’s PowerPoint 기존 추천 시스템 • 뉴스 검색 기록의 1차원 벡터화 • 뉴스 기록에 잠재된 사용자의 흥미를 후보 뉴 스군에 매칭 하기 어려움 • Okura et al 2017, Wu et al 2019, FIM • 기존 추천 시스템의 문제 해결 1. 1차원의 벡터대신 convolution 사용으로 사용자에 대한 관심에 알맞는 뉴스 추천 가능 2. 각 뉴스와 후보 뉴스를 단어 , 문장 대한 더 많은 정보 학습
ⓒSaebyeol Yu. Saebyeol’s PowerPoint 사용자의 특성을 추출하는 대신, 뉴스를 다양한 레벨에서 Matching시켜, 다양한측면에서비교하여더욱정확한정 보를추려낼수있다. “
ⓒSaebyeol Yu. Saebyeol’s PowerPoint Overview >> >> >> Step 1 Step 2 Step 3 Step 4 Problem Definition • 사용자별뉴스검 색기록추출 • WordEmbeeding News Representation • Hierarchical dilated convolution • 3D matrix 형태뉴 스 변형 Part 1 Cross Matching Matrices • 후보뉴스 X 뉴 스 기록 Click Prediction Module • Convolution Aggregate • 3D CNN
ⓒSaebyeol Yu. Saebyeol’s PowerPoint Step 1 : 문제 정의 Part 1 1 사용자 u 에대한뉴스검색기록 𝑆𝑢= {𝑑1,…𝑑𝑛} 3 후보영화𝐶𝑖,예측값 : 𝑦𝑖 ⊆{0,1}정의 4 g:𝑆𝑢,𝐶 𝑦̂ 사용자의뉴스탐색기록u와,후보영화를입력했을때,예측값이나올함수g 제목, Category, Subcategory  d = emb([x1,…𝑥𝑛]) 2
ⓒSaebyeol Yu. Saebyeol’s PowerPoint Dataset Setting Microsoft News dataset Glove Embedding 20 length of sent + cat + subcat 50개의 뉴스 검색 리스트
ⓒSaebyeol Yu. Saebyeol’s PowerPoint • d-dimension embedding d_i • Heirarchcial dilated convolution Layers • 텍스트에서 다양한 의미를 찾을수 있다 • Standard Convolution 과 차이점 : • 𝛿 만큼의 element 를 filed에서 스킵하 면서 학습 , 다양한 의미파악을 위해 Multi level 𝛿 사용 • 각 layer 마다 Normal Batch 적용 • 넓은 View , 적은 계산 비용 , no loss in information Step 2-1 : Hierarchical dilated convolution
ⓒSaebyeol Yu. Saebyeol’s PowerPoint • 각Layer에다양한level의Mutileveldilation사용 • 각각Layer에NormalBatch사용 • 각Layer을쌓아줌 • 단어,문장,cateogory등다양한의미를학습이가능 • Max-pooling과같은downsampling이없어서정보소실가능 성이적음 Step2-2:Multi-grainedNews Representation
ⓒSaebyeol Yu. Saebyeol’s PowerPoint • K개의History뉴스와후보뉴스C의scaleddotproduct • <d_k,c>각각History와후보뉴스의곱을통해각채 널별(문장군,단어군등)중요한정보의곱으로구현 Step3:CrossMatchingModule
ⓒSaebyeol Yu. Saebyeol’s PowerPoint • 앞서 내적한 문서 I X 후보뉴스 의 3D convolutions 들 을요약하기위해,모두 조합하여3Dmatching image Q로만들어줌 Step4:Convolution Aggregate
ⓒSaebyeol Yu. Saebyeol’s PowerPoint • 3D convolution • filters: 32-[3,3,3], 16-[3,3,3] • Stride : [1,1,1] • max pooling network 로 전체 이미지에서 의미 있는 (특이한) 시그널을 찾음 [3,3,3] • Output : 사용자의 기록 리스트와 후보 뉴스에 대한 Matching 벡터 • 추천시스템의 결과 : 후보 뉴스의 추천 랭킹 • 해당 후보 뉴스의 Click probability 계산 • Optimizer : Adam , mini-batch 100 , initial learning rate 1e-3 Step 2-1 : Click Prediction Module
ⓒSaebyeol Yu. Saebyeol’s PowerPoint ComparisonMethods Part 2 Manually Feature-based Methods Neural Recommendation Methods LibFm(Rendel2012):matrixfactorization DFM(Lian2018):denselayer+Attention DSSM(Huang2013): trigram+multidenselayer DKN(2018):Knoledgegraph+CNN Wide&Deep(Cheng2016): matrixfactorization+denselayer GRU(Okura2017):Auto-encoder+GRU DeepFM(Guo2017): factorizationmachine+DNN NRU(Wu2019):Multi-headAttention NPA(Wu2019):PersonalizeAttention
ⓒSaebyeol Yu. Saebyeol’s PowerPoint ComparisonMethods Part 3 • Neural > Traditional • Deep Neural Model 이사용자와뉴스사이 의의미있는Feature을더잘분석 • FIM > Neural > Taditional • SOTA
ⓒSaebyeol Yu. Saebyeol’s PowerPoint Quantity&InputAnalysis Part 2
ⓒSaebyeol Yu. Saebyeol’s PowerPoint Quantity&InputAnalysis Part 2
ⓒSaebyeol Yu. Saebyeol’s PowerPoint Part 5 Visualization
ⓒSaebyeol Yu. Saebyeol’s PowerPoint 감사합니다

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Fine –grained interest matching for neural news recommendation

  • 1. ⓒSaebyeol Yu. Saebyeol’s PowerPoint Fine –grained Interest Matching For Neural News Recommendation 펀다멘탈팀 김지연, 김동희, 김창연, 송헌, 이근배, 이재윤
  • 2. ⓒSaebyeol Yu. Saebyeol’s PowerPoint • 새로 생성되는 방대한 뉴스의 양 • 각 개인의 다양한 흥미 분야 • 기존 추천시스템의 구조적인 한계 개인 맞춤 뉴스 추천 시스템
  • 3. ⓒSaebyeol Yu. Saebyeol’s PowerPoint Related Work Part 2 Conventional Methods Deep Learning Methods • User-News Feature engineering (Phelan et al 2011) • Topic category and Feature by Bayesian (Liu et al 2013 etc.) • location based news recommendation (Son et al 2013) • Distiling Semantic Feature through auto- encoder + RNN ( Okura et al) • Extract entity thorugh knowledge graph (Wu et al 2018 ) • GRU network to long0term user preference (An et al.)
  • 4. ⓒSaebyeol Yu. Saebyeol’s PowerPoint 기존 추천 시스템 • 뉴스 검색 기록의 1차원 벡터화 • 뉴스 기록에 잠재된 사용자의 흥미를 후보 뉴 스군에 매칭 하기 어려움 • Okura et al 2017, Wu et al 2019, FIM • 기존 추천 시스템의 문제 해결 1. 1차원의 벡터대신 convolution 사용으로 사용자에 대한 관심에 알맞는 뉴스 추천 가능 2. 각 뉴스와 후보 뉴스를 단어 , 문장 대한 더 많은 정보 학습
  • 5. ⓒSaebyeol Yu. Saebyeol’s PowerPoint 사용자의 특성을 추출하는 대신, 뉴스를 다양한 레벨에서 Matching시켜, 다양한측면에서비교하여더욱정확한정 보를추려낼수있다. “
  • 6. ⓒSaebyeol Yu. Saebyeol’s PowerPoint Overview >> >> >> Step 1 Step 2 Step 3 Step 4 Problem Definition • 사용자별뉴스검 색기록추출 • WordEmbeeding News Representation • Hierarchical dilated convolution • 3D matrix 형태뉴 스 변형 Part 1 Cross Matching Matrices • 후보뉴스 X 뉴 스 기록 Click Prediction Module • Convolution Aggregate • 3D CNN
  • 7. ⓒSaebyeol Yu. Saebyeol’s PowerPoint Step 1 : 문제 정의 Part 1 1 사용자 u 에대한뉴스검색기록 𝑆𝑢= {𝑑1,…𝑑𝑛} 3 후보영화𝐶𝑖,예측값 : 𝑦𝑖 ⊆{0,1}정의 4 g:𝑆𝑢,𝐶 𝑦̂ 사용자의뉴스탐색기록u와,후보영화를입력했을때,예측값이나올함수g 제목, Category, Subcategory  d = emb([x1,…𝑥𝑛]) 2
  • 8. ⓒSaebyeol Yu. Saebyeol’s PowerPoint Dataset Setting Microsoft News dataset Glove Embedding 20 length of sent + cat + subcat 50개의 뉴스 검색 리스트
  • 9. ⓒSaebyeol Yu. Saebyeol’s PowerPoint • d-dimension embedding d_i • Heirarchcial dilated convolution Layers • 텍스트에서 다양한 의미를 찾을수 있다 • Standard Convolution 과 차이점 : • 𝛿 만큼의 element 를 filed에서 스킵하 면서 학습 , 다양한 의미파악을 위해 Multi level 𝛿 사용 • 각 layer 마다 Normal Batch 적용 • 넓은 View , 적은 계산 비용 , no loss in information Step 2-1 : Hierarchical dilated convolution
  • 10. ⓒSaebyeol Yu. Saebyeol’s PowerPoint • 각Layer에다양한level의Mutileveldilation사용 • 각각Layer에NormalBatch사용 • 각Layer을쌓아줌 • 단어,문장,cateogory등다양한의미를학습이가능 • Max-pooling과같은downsampling이없어서정보소실가능 성이적음 Step2-2:Multi-grainedNews Representation
  • 11. ⓒSaebyeol Yu. Saebyeol’s PowerPoint • K개의History뉴스와후보뉴스C의scaleddotproduct • <d_k,c>각각History와후보뉴스의곱을통해각채 널별(문장군,단어군등)중요한정보의곱으로구현 Step3:CrossMatchingModule
  • 12. ⓒSaebyeol Yu. Saebyeol’s PowerPoint • 앞서 내적한 문서 I X 후보뉴스 의 3D convolutions 들 을요약하기위해,모두 조합하여3Dmatching image Q로만들어줌 Step4:Convolution Aggregate
  • 13. ⓒSaebyeol Yu. Saebyeol’s PowerPoint • 3D convolution • filters: 32-[3,3,3], 16-[3,3,3] • Stride : [1,1,1] • max pooling network 로 전체 이미지에서 의미 있는 (특이한) 시그널을 찾음 [3,3,3] • Output : 사용자의 기록 리스트와 후보 뉴스에 대한 Matching 벡터 • 추천시스템의 결과 : 후보 뉴스의 추천 랭킹 • 해당 후보 뉴스의 Click probability 계산 • Optimizer : Adam , mini-batch 100 , initial learning rate 1e-3 Step 2-1 : Click Prediction Module
  • 14. ⓒSaebyeol Yu. Saebyeol’s PowerPoint ComparisonMethods Part 2 Manually Feature-based Methods Neural Recommendation Methods LibFm(Rendel2012):matrixfactorization DFM(Lian2018):denselayer+Attention DSSM(Huang2013): trigram+multidenselayer DKN(2018):Knoledgegraph+CNN Wide&Deep(Cheng2016): matrixfactorization+denselayer GRU(Okura2017):Auto-encoder+GRU DeepFM(Guo2017): factorizationmachine+DNN NRU(Wu2019):Multi-headAttention NPA(Wu2019):PersonalizeAttention
  • 15. ⓒSaebyeol Yu. Saebyeol’s PowerPoint ComparisonMethods Part 3 • Neural > Traditional • Deep Neural Model 이사용자와뉴스사이 의의미있는Feature을더잘분석 • FIM > Neural > Taditional • SOTA
  • 16. ⓒSaebyeol Yu. Saebyeol’s PowerPoint Quantity&InputAnalysis Part 2
  • 17. ⓒSaebyeol Yu. Saebyeol’s PowerPoint Quantity&InputAnalysis Part 2
  • 18. ⓒSaebyeol Yu. Saebyeol’s PowerPoint Part 5 Visualization
  • 19. ⓒSaebyeol Yu. Saebyeol’s PowerPoint 감사합니다