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楽天市場データ + 機械学習を用いた予測事例の紹介 梅田卓志/楽天株式会社

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2019/3/28に開催したRakuten Tech Meetup #1 事業に響くデータとAIの発表資料です。

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楽天市場データ + 機械学習を用いた予測事例の紹介 梅田卓志/楽天株式会社

  1. 1. 楽天市場データ+機械学習による 予測事例の紹介 Takashi Umeda (梅田卓志) Rakuten Institute of Technology Rakuten Inc. • LinkedIn: https://jp.linkedin.com/in/takashi-umeda-a291808 • Eight: https://8card.net/p/39626131477 • Twitter: @umekoumeda
  2. 2. 2 梅田 卓志 (Takashi Umeda) 2016-2018: Finance • Investment • Credit Scoring • Support of marketing activities 2014-2015: Digital Contents • Recommender / Personalization • Video popularity prediction 2009-2013: EC • Demand forecasting • Prediction for users’ purchase timing
  3. 3. 3 What is “Rakuten Institute of Technology”?
  4. 4. 4 Location & members Strategic R&D organization in Rakuten Group +150 researchers all over the world Japan USSingapore France India
  5. 5. 5 Research Area of RIT 3 research groups for adapting to internet growth RealityIntelligencePower • HCI • MR / AR • Image Processing • Robotics • HPC • Distributed Computing • Programing Language • Machine Learning • Deep Learning • NLP • Data Mining
  6. 6. 6 Research Area of RIT 3 research groups for adapting to internet growth RealityIntelligencePower • HCI • MR / AR • Image Processing • Robotics • HPC • Distributed Computing • Programing Language • Machine Learning • Deep Learning • NLP • Data Mining
  7. 7. 7 Mission of Intelligence Domain Group Assist Rakuten businesses to boost with ML and DM Optimizing A/B testing Item Classification User Segmentation Data + AI Coupon Distribution Recommender System Economy Prediction / Demand Prediction Review Analysis Anomaly Detection / Fraud Detection Product Data Analysis Business
  8. 8. 8 Case-1 Prospective User Extraction
  9. 9. 9 Mission of Intelligence Domain Group Assist Rakuten businesses to boost with ML and DM Optimizing A/B testing Item Classification User Segmentation Data + AI Coupon Distribution Recommender System Economy Prediction / Demand Prediction Review Analysis Anomaly Detection / Fraud Detection Product Data Analysis Business
  10. 10. 10 Goal Ichiba Active Users Prospective users Extract Financial Service Find out particular financial service users from ICHIBA users
  11. 11. 11 Approach Ichiba Active Users Overlap 7,413 Positive Samples 7,417 Negative Samples About 50% of the financial service users were Ichiba Active Users Financial Service Users
  12. 12. 12 Model Input Interest rate Predict who will use particular financial service • Purchase trend • Purchase of each L2 genre • GMS trend, Frequency • Basic Demogra. • Gender, Area, Age Model Output Interest rate Will be user Won’t be user
  13. 13. 13 What kinds of Rakuten ICHIBA users are most likely to apply for the target service?
  14. 14. 14 Significant Factors 0 0.05 0.1 0.15 0.2 0.25 genre_41_100890_ / 花・ガーデン・DIY / DIY・工具 genre_72_111078_ / キッズ・ベビー・マタニティ / キッズ genre_50_110983_ / 靴 / メンズ靴 Age-05-[35-40] genre_93_101077_ / スポーツ・アウトドア / ゴルフ Area-01-Kanto Area-00-Others genre_113_101126_ / 車用品・バイク用品 / カー用品 Age-08-[50-*] Age-03-[25-30] Age-00-none gms Gender-00-none basket_max_price frequency basket_average_price average_unit_price Age-02-[20-25] Gender-02-female Gender-01-male Top 20 factors selected from 141 factors Ichiba Genre /Cars & Motorcycles/Car Accessories Ichiba Genre /Sports & Outdoors/Golf Ichiba Genre /Shoes/Men’s Shoes Ichiba Genre /Kids & Baby/Kids Ichiba Genre /Gardening & Tools / DIY Tools average_unit_price basket_average_price frequency basket_max_price gms Loyalty Life Stage
  15. 15. 15 Evaluation #1 Prospective Users Control Group • Randomly Selected • About 300,000 users • Score >= 0.8 • About 300,000 users Send ichiba mail magazine to two groups Ichiba Mail Magazine
  16. 16. 16 Evaluation #2 Mail Deliver Open Mail Click Contents (Visit Service Page) Click Rate went up by +49.23% compared with control group +3.52% +49.23%
  17. 17. 17 Application in other services Prospective users Extract Target service Same scheme can be applied to various kinds of services Ichiba Active Users
  18. 18. 18 Platform-nization Algorithm: RIT Platform: Data Science Dep. Rakuten Businesses National Clients Customer DNA • Input: Users who uses the particular service • Output: Users who will use that service
  19. 19. 19 Platform-nization Algorithm / Model : RIT Platform / Interface : DSD Rakuten Businesses National Clients Customer DNA • Creating a platform for the solution together with GDSD. • Integrating Customer DNA for User Features
  20. 20. 20 Current statistics (1/2) National Clients
  21. 21. 21 Current statistics (2/2) https://corp.rakuten.co.jp/news/update/2018/0522_01.html accessed on 2019-03-25
  22. 22. 22 Case-2 Prediction of JP Economy
  23. 23. 23 Mission of Intelligence Domain Group Assist Rakuten businesses to boost with ML and DM Optimizing A/B testing Item Classification User Segmentation Data + AI Coupon Distribution Recommender System Economy Prediction / Demand Prediction Review Analysis Anomaly Detection / Fraud Detection Product Data Analysis Business
  24. 24. 24 Question 70 75 80 85 90 95 100 105 110 115 120 2008-1 2008-4 2008-7 2008-10 2009-1 2009-4 2009-7 2009-10 2010-1 2010-4 2010-7 2010-10 2011-1 2011-4 2011-7 2011-10 2012-1 2012-4 2012-7 2012-10 2013-1 2013-4 2013-7 2013-10 2014-1 CompositeIndex Month Composite Index (景気動向指数) ?Abenomics LEHMAN Shock Japanese economy will be better ?
  25. 25. 25 Objective Predict Japanese economy by Rakuten’s data Composite Index (景気動向指数) Effect JP Economy
  26. 26. 26 Objective Predict Japanese economy by Rakuten’s data Composite Index (景気動向指数) Effect JP Economy Sales of each category
  27. 27. 27 Model • Predict Composite Index by using LASSO • Use monthly sales data in each L4 genre Effect Sales at t (genre a) Composite Index at t (t 期の景気動向指数) LASSO Sales at t (genre b) Sales at t (genre c) : L4 genres, 2521 genres
  28. 28. 28 Prediction Results Mean absolute error is about 0.4 • Training data : Dec., 2009 – Dec. 2012 • Test data : Jan., 2013 – Apr., 2013 94 96 98 100 102 104 106 108 110 CompositeIndex(CI) Month Prediction of Composite Index Actual Predict(Training fit) Predict(Test fit)
  29. 29. 29 What kinds of categories are affecting Japanese Economy?
  30. 30. 30 Effective Predictors jewelry (Cameo) Air Conditione r (窓用エアコン) PC (Workstation) Comedy (Blu-ray) If following genre sales become larger, composite Index will be larger.
  31. 31. 31 Summary
  32. 32. 32 Mission of Intelligence Domain Group Assist Rakuten businesses to boost with ML and DM Optimizing A/B testing Item Classification User Segmentation Data + AI Coupon Distribution Recommender System Economy Prediction / Demand Prediction Review Analysis Anomaly Detection / Fraud Detection Product Data Analysis Business
  33. 33. 33 See you later in the round table discussion! Data • Contribution for existing business • New Business Dev. AI

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