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Session-based recommendations with recurrent neural networks

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Zimin Park

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SESSION - BASED RECOMMENDATIONS WITH RECURRENT NEURAL NETWORKS ICLR 2016
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Balázs Hidasi Gravity R&D , Head of Data Mining and Research Ph.D Context-aware factorization methods for implicit feedback based recommendation problems(2016) Alexandros Karatzoglou Google, Staff Research Scientist (19. 9 ~) Telefonica Research, Scientific Director Cowork ● Parallel Recurrent Neural Network Architectures for Feature-rich Session-based Recommendations(RecSys16) ● Recurrent Neural Networks with Top-k Gains for Session-based Recommendations(CIKM ’18) Author
About us Gravity is a personalization engine vendor offering a product portfolio called Yusp, filling multiple needs using the same underlying technology. Mission To enable businesses to be relevant and not forgotten. Gravity R&D Inc. 출처 :https://www.yusp.com/
Gravity R&D Inc. 출처 :https://www.yusp.com/
출처 :https://ko.wikipedia.org/wiki/%ED%85%94%EB%A0%88%ED%8F%AC%EB%8B%88%EC%B9%B4
● In e-commerce system, ● hard to track users ● even possible - only one or two sessions - should be handled independently ● hard to use MF and not accurate ● use item-to-item similarity - while effective - only taking into account the last click of the user, ignoring the past clicks
● 화장품 -> 청소기 -> 우유 -> 옷 을 조회했어도 ● 앞에 정보 무시 옷 관련만 추천 Related Work : Co-Occurence, Item2Vec 출처 https://brunch.co.kr/@goodvc78/16
Extended version of General Factorization Framework - two kinds of latent representation for items - representation as the item itself - representation as part of a session - does not consider ordering within the session 🤔 출처: https://medium.com/snipfeed/time-dependent-recommender-systems-part-2-92e8dfaf1199
Contribution - RNN 처음으로 사용 ( 딥러닝 도입 시도는 있었음.) - Session-Parallel Mini-batch - Sampling on the output - Ranking Loss
Model Architecture Model I/O Input : item click(1-of-N Encoding) or event(weighted sum of items) Output : item of the next event
without embedding, 1-of-N encoding is always better. Model I/O 1-of-N encoding weighted sum of items
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Model Architecture : GRU(Gated Recurrent Unit) 기본적인 RNN 구조 출처: https://towardsdatascience.com/illustrated-guide-to-lstms-and-gru-s-a-step-by-step-explanation-44e9eb85bf21
Model Architecture : GRU(Gated Recurrent Unit) 실험결과 Classic RNN, LSTM보다 GRU가 좋음 출처: https://towardsdatascience.com/illustrated-guide-to-lstms-and-gru-s-a-step-by-step-explanation-44e9eb85bf21
SESSION-PARALLEL MINI-BATCHES 1) length different even more than sentence some sessions consists of only 2 events 2) to capture how a session evolves over time Sessions are assumed to be independent reset the appropriate hidden state when switch occurs -> 코드 구현 화이팅...
Sampling on the output 출처 :Deep Neural Networks for YouTube Recommendations Why? ● Calculating a score for each item is unusable ● have to sample for calculating score How? ● Based on popularity ● Implicit data ● unseen data →dislike? didn’t know? ● 아이유 새 앨범 : 관심없어서인지 몰라서인지
Sampling on the output 출처 :https://instagram-engineering.com/powered-by-ai-instagrams-explore-recommender-system-7ca901d2a882
Ranking Loss Input : 킬링 이브 -> 비포 선셋 -> 엘르 -> 봄날은 간다 -> 멜로가 체질 -> ?? Output Ground Truth : 해리샐리 Candidates : [독전, 해리샐리, 황해, 조커, 비포 선라이즈] Predictions : [0.1, 0.8, 0.2, 0.5, 0.9] After Sorting : [비포 선라이즈, 해리샐리, 조커, 황해, 독전] Loss Pointwise : 해리샐리는 1로 나머지는 0으로. Pairwise : (해리샐리, 독전) , (해리샐리, 황해), (해리샐리, 조커), (해리샐리, 비포 선라이즈) 값 차이가 커지게! Listwise : [비포 선라이즈, 해리샐리, 조커, 황해, 독전] -> [해리샐리, 비포 선라이즈, 조커, 황해, 독전]로 만들도록! → Sorting 필요, O(NlogN) → Nope!
Bayesian Personalized Ranking UAI 2009 출처 : https://arxiv.org/pdf/1205.2618.pdf - When Dealing with Implicit Data - Seen data should be higher than unseen data - we don’t know ranking b/w seen data - b/w unseen data also
Bayesian Personalized Ranking UAI 2009 출처 : https://arxiv.org/pdf/1205.2618.pdf
BPR - max Recurrent Neural Networks with Top-k Gains for Session-based Recommendations 출처 : https://www.slideshare.net/balazshidasi/gru4rec-v2-recurrent-neural-networks-with-topk-gains-for-sessionbased-recommendations
Experiments - Datasets (RSC15) ● 전자상거래에서 유저들의 클릭 기록과 구매 기록을 모은 데이터. ● 논문에서는 클릭데이터만 사용 ● 길이 1짜리는 제외 6달치 데이터를 사용. ● Train Set : 7,966,257 sessions of 31,637,239 clicks on 37,483 item → 3.97 clicks / 1 session ● 학습 Session의 subsequent day data들이 Test Set. ● Train Set에 없는 Test Set의 Item들은 전부 제거 ● Test Set : 15,324 sessions of 71,222 events for the test set → 4.64 clicks/ 1session
● Youtube Like Service ● RSC15와 비슷함 ● item2item 추천이 돌아가고 있어서 유저의 행동에 영향을 주었을 거라 의심 Selection Bias? ● Session이 너무 긴거는 Bot일거라고 의심 Experiments - Datasets (youtube like OTT)
Metric : Recall@20 and MRR@20 출처: https://ko.wikipedia.org/wiki/%EC%A0%95%EB%B0%80%EB%8F%84%EC%99%80_%EC%9E%AC%ED%98%84%EC%9C%A8 https://www.blabladata.com/2014/10/26/evaluating-recommender-systems/
Baselines Experiments Item - KNN이 압도적 → Baseline Model
GRU Model Experiments
● weight initialization : [-x, x] uniformly ● adagrad >rmsprop ● GRU > LSTM, RNN ● Single GRU Layer is Enough (아마도 session 이 비교적 짧아서) ● GRU Size를 늘리는건 도움 ● activation 으로 tanh 로 쓰는게 좋음100/ 1000 unit 학습, 추론 속도 차이 크게 없음 ● 자주 재학습이 가능한지는 추천 시스템에서 중요 ● KNN 보다 훨씬 좋음 ● Cross - entropy는 Pair-wise loss아님 ● CE의 경우 unit 늘리면 떨어짐 ● Top1 , BPR 크게 차이 없음 GRU Model Experiments
Author Code 저자가 공개한 코드 with THEANO : https://github.com/hidasib/GRU4Rec 이 코드의 간단한 리뷰 : https://www.notion.so/zmin/Code-Review-Session-Based-RecSys-7edaa306e3424426aa6956e903fdcc2a 이 코드를 Keras 로 구현한 Repo : https://github.com/pcerdam/KerasGRU4Rec
From RNN to Transformer 출처 :Behavior Sequence Transformer for E-commerce Recommendation in Alibaba
From RNN to CNN

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Session-based recommendations with recurrent neural networks

  • 1. SESSION - BASED RECOMMENDATIONS WITH RECURRENT NEURAL NETWORKS ICLR 2016
  • 3. Balázs Hidasi Gravity R&D , Head of Data Mining and Research Ph.D Context-aware factorization methods for implicit feedback based recommendation problems(2016) Alexandros Karatzoglou Google, Staff Research Scientist (19. 9 ~) Telefonica Research, Scientific Director Cowork ● Parallel Recurrent Neural Network Architectures for Feature-rich Session-based Recommendations(RecSys16) ● Recurrent Neural Networks with Top-k Gains for Session-based Recommendations(CIKM ’18) Author
  • 4. About us Gravity is a personalization engine vendor offering a product portfolio called Yusp, filling multiple needs using the same underlying technology. Mission To enable businesses to be relevant and not forgotten. Gravity R&D Inc. 출처 :https://www.yusp.com/
  • 5. Gravity R&D Inc. 출처 :https://www.yusp.com/
  • 7. ● In e-commerce system, ● hard to track users ● even possible - only one or two sessions - should be handled independently ● hard to use MF and not accurate ● use item-to-item similarity - while effective - only taking into account the last click of the user, ignoring the past clicks
  • 8.
  • 9. ● 화장품 -> 청소기 -> 우유 -> 옷 을 조회했어도 ● 앞에 정보 무시 옷 관련만 추천 Related Work : Co-Occurence, Item2Vec 출처 https://brunch.co.kr/@goodvc78/16
  • 10. Extended version of General Factorization Framework - two kinds of latent representation for items - representation as the item itself - representation as part of a session - does not consider ordering within the session 🤔 출처: https://medium.com/snipfeed/time-dependent-recommender-systems-part-2-92e8dfaf1199
  • 11. Contribution - RNN 처음으로 사용 ( 딥러닝 도입 시도는 있었음.) - Session-Parallel Mini-batch - Sampling on the output - Ranking Loss
  • 12. Model Architecture Model I/O Input : item click(1-of-N Encoding) or event(weighted sum of items) Output : item of the next event
  • 13. without embedding, 1-of-N encoding is always better. Model I/O 1-of-N encoding weighted sum of items
  • 15.
  • 16. Model Architecture : GRU(Gated Recurrent Unit) 기본적인 RNN 구조 출처: https://towardsdatascience.com/illustrated-guide-to-lstms-and-gru-s-a-step-by-step-explanation-44e9eb85bf21
  • 17. Model Architecture : GRU(Gated Recurrent Unit) 실험결과 Classic RNN, LSTM보다 GRU가 좋음 출처: https://towardsdatascience.com/illustrated-guide-to-lstms-and-gru-s-a-step-by-step-explanation-44e9eb85bf21
  • 18. SESSION-PARALLEL MINI-BATCHES 1) length different even more than sentence some sessions consists of only 2 events 2) to capture how a session evolves over time Sessions are assumed to be independent reset the appropriate hidden state when switch occurs -> 코드 구현 화이팅...
  • 19. Sampling on the output 출처 :Deep Neural Networks for YouTube Recommendations Why? ● Calculating a score for each item is unusable ● have to sample for calculating score How? ● Based on popularity ● Implicit data ● unseen data →dislike? didn’t know? ● 아이유 새 앨범 : 관심없어서인지 몰라서인지
  • 20. Sampling on the output 출처 :https://instagram-engineering.com/powered-by-ai-instagrams-explore-recommender-system-7ca901d2a882
  • 21. Ranking Loss Input : 킬링 이브 -> 비포 선셋 -> 엘르 -> 봄날은 간다 -> 멜로가 체질 -> ?? Output Ground Truth : 해리샐리 Candidates : [독전, 해리샐리, 황해, 조커, 비포 선라이즈] Predictions : [0.1, 0.8, 0.2, 0.5, 0.9] After Sorting : [비포 선라이즈, 해리샐리, 조커, 황해, 독전] Loss Pointwise : 해리샐리는 1로 나머지는 0으로. Pairwise : (해리샐리, 독전) , (해리샐리, 황해), (해리샐리, 조커), (해리샐리, 비포 선라이즈) 값 차이가 커지게! Listwise : [비포 선라이즈, 해리샐리, 조커, 황해, 독전] -> [해리샐리, 비포 선라이즈, 조커, 황해, 독전]로 만들도록! → Sorting 필요, O(NlogN) → Nope!
  • 22. Bayesian Personalized Ranking UAI 2009 출처 : https://arxiv.org/pdf/1205.2618.pdf - When Dealing with Implicit Data - Seen data should be higher than unseen data - we don’t know ranking b/w seen data - b/w unseen data also
  • 23. Bayesian Personalized Ranking UAI 2009 출처 : https://arxiv.org/pdf/1205.2618.pdf
  • 24. BPR - max Recurrent Neural Networks with Top-k Gains for Session-based Recommendations 출처 : https://www.slideshare.net/balazshidasi/gru4rec-v2-recurrent-neural-networks-with-topk-gains-for-sessionbased-recommendations
  • 25. Experiments - Datasets (RSC15) ● 전자상거래에서 유저들의 클릭 기록과 구매 기록을 모은 데이터. ● 논문에서는 클릭데이터만 사용 ● 길이 1짜리는 제외 6달치 데이터를 사용. ● Train Set : 7,966,257 sessions of 31,637,239 clicks on 37,483 item → 3.97 clicks / 1 session ● 학습 Session의 subsequent day data들이 Test Set. ● Train Set에 없는 Test Set의 Item들은 전부 제거 ● Test Set : 15,324 sessions of 71,222 events for the test set → 4.64 clicks/ 1session
  • 26. ● Youtube Like Service ● RSC15와 비슷함 ● item2item 추천이 돌아가고 있어서 유저의 행동에 영향을 주었을 거라 의심 Selection Bias? ● Session이 너무 긴거는 Bot일거라고 의심 Experiments - Datasets (youtube like OTT)
  • 27. Metric : Recall@20 and MRR@20 출처: https://ko.wikipedia.org/wiki/%EC%A0%95%EB%B0%80%EB%8F%84%EC%99%80_%EC%9E%AC%ED%98%84%EC%9C%A8 https://www.blabladata.com/2014/10/26/evaluating-recommender-systems/
  • 28. Baselines Experiments Item - KNN이 압도적 → Baseline Model
  • 30. ● weight initialization : [-x, x] uniformly ● adagrad >rmsprop ● GRU > LSTM, RNN ● Single GRU Layer is Enough (아마도 session 이 비교적 짧아서) ● GRU Size를 늘리는건 도움 ● activation 으로 tanh 로 쓰는게 좋음100/ 1000 unit 학습, 추론 속도 차이 크게 없음 ● 자주 재학습이 가능한지는 추천 시스템에서 중요 ● KNN 보다 훨씬 좋음 ● Cross - entropy는 Pair-wise loss아님 ● CE의 경우 unit 늘리면 떨어짐 ● Top1 , BPR 크게 차이 없음 GRU Model Experiments
  • 31. Author Code 저자가 공개한 코드 with THEANO : https://github.com/hidasib/GRU4Rec 이 코드의 간단한 리뷰 : https://www.notion.so/zmin/Code-Review-Session-Based-RecSys-7edaa306e3424426aa6956e903fdcc2a 이 코드를 Keras 로 구현한 Repo : https://github.com/pcerdam/KerasGRU4Rec
  • 32. From RNN to Transformer 출처 :Behavior Sequence Transformer for E-commerce Recommendation in Alibaba
  • 33. From RNN to CNN