In this talk, we introduced our innovative ways to collaborate with a variety of businesses, academia, clients and startups in order to create new social value.
We focus on data, discuss with business people, invent new ideas with clients and challenge social innovation with startups.
2019年07月09日 リカレントエデュケーション講座@京橋。
楽天ではどのようにビッグデータを活用しているのか、データサイエンス&AIの最新応用事例の紹介。
およびデータサイエンス系のプロジェクトの進め方と,必要な役割についての紹介。
登壇者:平手勇宇(Rakuten Institute of Technology Tokyo)
2019年07月09日 リカレントエデュケーション講座@京橋。
楽天ではどのようにビッグデータを活用しているのか、データサイエンス&AIの最新応用事例の紹介。
およびデータサイエンス系のプロジェクトの進め方と,必要な役割についての紹介。
登壇者:平手勇宇(Rakuten Institute of Technology Tokyo)
2019年10月25日、CTC Forum 2019@品川。楽天ではどのようにビッグデータの活用を行っているのか、データサイエンスおよびAIの視点でプレゼンテーションが行われた。登壇者:勝山 公雄(Senior Manager, Global Data Supervisory Department, Rakuten, Inc.)
2019年10月25日、CTC Forum 2019@品川。楽天ではどのようにビッグデータの活用を行っているのか、データサイエンスおよびAIの視点でプレゼンテーションが行われた。登壇者:勝山 公雄(Senior Manager, Global Data Supervisory Department, Rakuten, Inc.)
This document discusses how to make software more green and environmentally friendly. It defines green software as software that is carbon efficient, energy efficient, hardware efficient, and carbon aware. It provides recommendations for various roles within an organization on driving green initiatives, including focusing on efficiency for CxOs, architects, infrastructure engineers, and developers. Examples include optimizing resource usage, using public clouds effectively, prioritizing equipment standardization, and developing applications that can run more efficiently.
Simple and Effective Knowledge-Driven Query Expansion for QA-Based Product At...Rakuten Group, Inc.
The document proposes a knowledge-driven query expansion approach for question answering (QA)-based product attribute extraction. It trains QA models using attribute-value pairs from training data as knowledge, while mimicking imperfect knowledge at test time through techniques like knowledge dropout and token mixing. This helps induce better query representations, especially for rare and ambiguous attributes. Experiments on a cleaned product attribute dataset show the proposed approach with all techniques outperforms baseline methods in both macro and micro F1 scores.
This document summarizes Andrew Hajinikitas' work developing Rakuten's private cloud infrastructure. It describes the key components of Rakuten's infrastructure including metal instances, microservers, and GPU servers. It provides details on Rakuten's software stack and their goals to expand managed services. Currently, Rakuten operates 9 data centers in Japan and overseas providing around 30,000 servers to support their ecosystem. Their future plans include extending network self-service, making GPU resources available as a platform service, and improving efficiency through optimized hardware selection.
The document discusses the Travel & Leisure Platform Dept and its responsibilities related to data and platform management. It provides an overview of the technical stack including private/public clouds, databases, containers, and automation/monitoring tools. It then discusses recent projects involving business continuity, containerization, alert integration, and automation. Finally, it describes open roles for a DBA and DevOps position and their responsibilities related to database provisioning, backup/recovery, infrastructure as code, and providing platforms and tools for developers.
This presentation introduces the OWASP Top 10:2021.
It explains how to look at the data related to OWASP Top 10:2021, and provides detailed explanations of items with distinctive data. It also introduces the OWASP Project related to each item.
Gora API Group technology provides a microservices architecture and APIs for Rakuten's golf course reservation system, improving the user experience and increasing customer loyalty and annual golf rounds. The architecture migrates the monolithic reservation system to microservices using Kotlin, Spring Boot, and other technologies, exposing APIs for the frontend and new products while sustaining the legacy system through services, queues, continuous delivery, and operations monitoring.
4. 4
楽天技術研究所:Rakuten Institute of Technology
http://rit.rakuten.co.jp/組織のVision については後ほど
世界5カ国。140名以上。事業とは独立した戦略的R&D組織。
研究者の問題意識・関心・やりたいに基づいた研究の推進と、研究者による評価
Bring new wind from Academia to Rakuten.
5. 5
Research Areas: 3 groups for adapting to internet growth
2017年は66個の研究 Prjが誕生。その内 42 Prj でビジネス成果を創出
RealityIntelligencePower
• HCI
• AR / VR / MR
• Image Processing
• Robotics
• HPC
• Distributed Computing
• Programing Language
• Machine Learning
• Deep Learning
• NLP
• Data Mining
15. 15
braking ball absolute ball position at plate
An example of analysis : clustering pitching ball
https://trackmanbaseball.zendesk.com/hc/en-us/articles/
205365001-What-is-TrackMan-Data-
Trackman
Clustering pitchers and batters
for predicting next pitching using Trackman Data.
【こんなデータも!】 楽天イーグルスでの投球予測の研究
23. 23
【さらに様々な連携を】 “Future Store Design Lab” (未来店舗デザイン研究室)
Nov. 4th (2016)
Corporate press release
http://corp.rakuten.co.jp/news/press/2016/1104_01.html
Launched a co-working space in Tsukuba Univ.
24. 24
Integration of Computer Science, Art and Design
Prof. Yamanaka
Executive Officer,
Provost of Faculty of Art and
Design
(大学執行役員、芸術系長)
Prof. Utsuro
NLP & Machine Learning
Assist. prof. Yamada
Product Design & Design
innovation
Assoc. prof. Hoshino
Entertainment computing,
Game technology
Assoc. prof. Uchiyama
Information Design & Product
Design
Prof. Uchida
Deputy Director for
International Innovation
Interface Organization
(国際産学連携本部 本部審議役)
Prof. Tanaka
Ubiquitous Computing
Computer Human Interaction
Software and Programming
Assoc. prof. Yamashita
Computer vision
Robotics
Machine Learning
Prof. Fujiyoshi
Computer vision
Robotics
Machine Learning
Prof. Koyama
Information design, consumer
behavior, perception and
cognition, neuropsychology
28. 28
Kaori ASANO
Zootie
Director
Collaborating with Ichiba’s Merchants
Enhancing In-store shopping experience by Technology
Naoyuki IDE
YO-HO Brewing company
President & Chief Executive Officer
Asaka KAWASE
NECO REPUBLIC
President & Chief Executive Officer