地理情報を活用したレコメンダーシステムの構築と評価
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Intelligence Domain Group, Rakuten Institute of Technology is working on developing various kinds of solutions utilizing Rakuten Data in order to assist Rakuten services. Now Rakuten Group has various kinds of data associated with geolocation information. If we utilize geolocation information, we will be able to enhance our marketing activities. In this presentation, we introduce an algorithm for geolocation based recommender system. We conducted a marketing test for Edy business and confirmed the efficiency of the algorithm.
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dataset & the performance of the models is checked again. Data Remodeling often plays a very crucial and
important role in boosting classifier accuracies by changing the properties of the given dataset. We then try
to explore and analyze the various reasons due to which one model performs better than the other & hence
try and develop an understanding about the applicability of linear & non-linear machine learning models.
The target mentioned above being our primary goal, we also aim to find the classifier with the best possible
accuracy for product sales estimation in the given scenario.
DTH Case Study
direct to home is latest trend followed by many companies & organizations.
To download visit:
blog.cequitysolutions.com and www.cequitysolutions.com
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Machine learning & artificial intelligence. Machine learning is playing an increasingly important role in computing and artificial intelligence. Suits any article on AI, algorithms, machine learning, quantum computing, artificial intelligence.
Machine learning training bootcamp is a 3-day technical training course that covers the fundamentals of machine learning, a form and application of artificial intelligence (AI).
Attendees will learn, comprehend and master ideas on machine learning concepts, key principles, techniques including: supervised and unsupervised learning, mathematical and heuristic aspects, modeling to develop algorithms, prediction, linear regression, clustering, classification, and prediction.
Learning Objectives:
Learn about Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL)
List similarities and differences between AI, Machine Learning and Data Mining
Learn how Artificial Intelligence uses data to offer solutions to existing problems
Explore how Machine Learning goes beyond AI to offer data necessary for a machine to learn.
Clarify how Data Mining can serve as foundation for AI and machine learning.
List the various applications of machine learning and related algorithms
Learn how to classify the types of learning such as supervised and unsupervised learning
Implement supervised learning techniques such as linear and logistic regression
Use unsupervised learning algorithms including deep learning, clustering , etc.
Learn about classification data and Machine Learning models
Select the best algorithms applied to Machine Learning
Learn the concepts and operation of support neural networks, vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means and clustering
more...
Course Agenda and Topics:
The Basics of Machine Learning
Machine Learning Techniques, Tools and Algorithms
Data and Data Science
Review of Terminology and Principles
Applied Artificial Intelligence (AI) and Machine Learning
Popular Machine Learning Methods
Learning Applied to Machine Learning
Principal Component Analysis
Principles of Supervised Machine Learning Algorithms
Principles of Unsupervised Machine Learning
Regression Applied to Machines Learning
Principles of Neural Networks
Large Scale Machine Learning
Introduction to Deep Learning
Applying Machine Learning
Overview of Algorithms
Overview of Tools and Processes
Call us today at +1-972-665-9786. Learn more about course audience, objectives, outlines, pricing. Visit our website links below.
Machine Learning Training Bootcamp
https://www.tonex.com/training-courses/machine-learning-training-bootcamp/
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** Machine Learning Master's Program: https://www.edureka.co/masters-program/machine-learning-engineer-training **
This Edureka PPT on "How to become a Machine Learning Engineer" covers all the basic aspects of becoming a certified Machine Learning Engineer. It establishes the concepts like roles, responsibilities, skills, salaries and even trends to get you up to speed with Machine learning.
Follow us to never miss an update in the future.
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地理情報を活用したレコメンダーシステムの構築と評価
- 2. 2 自己紹介 Master & Doctor of Engineering in Computational Intelligence and Systems Science Data scientist, Intelligence Domain Group 2015/04 2015/03 Present Joined Rakuten --recommender system, potential user extraction, etc
- 3. 3 Providing useful information, services based on user’s geographical data Customer Satisfaction Right service Right place Right time Happy customer
- 4. 4 >> make appropriate recommendations >> provide other options >> expand usage coverage >> extract potential cross-service users >> facilitate cross-use between services
- 6. 6 ➢ Accurate targeting Recommend shop with unused genre to user -- Improve user awareness, cross-use, NPS -- Facilitate promotion, GMS growth Target Requirements ➢ Effective recommendation Actions ➢ Active users who have major areas ➢ e-mail & push notification ● shop A ● shop B ● shop C ● shop D Office Home
- 7. 7 edyNo shopID timestamp … shopID coordinates name address genreID description … Total genre No. : ~100 Total shop No. : ~300,000
- 8. 8 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 1 2 3 4 5 6 7 8 9 10 11 The number of genres that user totally uses
- 9. 9 Transaction Geolocation …. MajorAreaGeneration RecommenderConstruction Shop list Genre list Model × recommended shops User ids Input Processing Output
- 10. 10 Activeness Filter Active users Transaction data Clustering (DBSCAN) For each user User’s location clusters Boundary building Shops extractionMajor areas Candidate shop list
- 11. 11 Parameter Value min_sample 3 max_distance 0.5 km DBSCAN 0% 5% 10% 15% 20% 25% 1 2 3 4 5 6 7 8 9 10 11 12 13 Major area number users have User number pecentage of each major area
- 13. 13 Step 1 : cluster used shops Step 4 : locate recommended shop Step 2 : label centroid, build boundary Step 3 : list all shops in major area
- 14. 14 Collaborative filtering Prediction Model Training data Target data Apply to Final result: User with one shop Select the genre with highest rating from recommendation results Find the suitable shop in major areas Candidate shop list From MajorAreaGeneration ✓
- 15. 15 Not able to accurately locate users Lack of consideration of shop location Previous ・Tokyo ・Kanagawa ・Saitama ・Chiba ・Osaka ・Okinawa User’s unused genres UserA UserB BA × Targeting by prefecture
- 16. 16 ✓User targeting based on transactions ✓Recommended shops in reachable range ■Automatic delivery ■Personalized targeting DB Algorithm X Batch × Delivery timing ==Auto & Accurate==
- 17. 17 0 0.0001 0.0002 0.0003 0.0004 0.0005 0.0006 0.0007 0.0008 Before After GMS/Delivered Emails Send emails to recommend supermarket to users within their major areas November, 2016 0.00% 2.00% 4.00% 6.00% 8.00% 10.00% 12.00% before after +10.0pt CVR x12.54
- 18. 18 Department store (2016/12)CVR (%) gms/uu (JPY) × × Fashion (2017/03)CVR (%) gms/uu (JPY) × ×
- 19. 19 Geolocation application in real time Collaboration with more services User’s viewpoint to merchant’s viewpoint
- 20. 20 Cross-use !!!
- 21. 21 The end