Personal Information
Organization / Workplace
Within 23 wards, Tokyo, Japan Japan
Occupation
Data Scientist
Industry
Technology / Software / Internet
About
My responsibilities are
- to deliver relevant ads or organic news article for docomo user
- to optimize advertiser's KPI(ROAS, CPA, CVR).
Through these job experiences, I have grown up the following four strong points, ①Service Strategy②Research③Development④Sales and Consulting.
①Service Strategy
Google has so sophiscated Sponsord Search ads, on the other hand we also have the product.
So, we have to think about why advertisers use D2C Sponsord Search ads.
I think the solution is so simple and it is just the more efficient performance compared with the Google product.
To do so, we have to hold some mathematical model to predict or recommend relevant information for many users.
②Researc...
Tags
regret minimization
See more
Presentations
(15)Documents
(4)Likes
(314)「ベータ分布の謎に迫る」第6回 プログラマのための数学勉強会 LT資料
Ken'ichi Matsui
•
8 years ago
動的計画法を極める!
HCPC: 北海道大学競技プログラミングサークル
•
7 years ago
最適化超入門
Takami Sato
•
9 years ago
Conditional Image Generation with PixelCNN Decoders
suga93
•
7 years ago
20170422 数学カフェ Part2
Kenta Oono
•
7 years ago
ConvNetの歴史とResNet亜種、ベストプラクティス
Yusuke Uchida
•
7 years ago
機械学習の精度と売上の関係
Tokoroten Nakayama
•
6 years ago
Recommender Systems: Advances in Collaborative Filtering
Changsung Moon
•
8 years ago
音源分離における音響モデリング(Acoustic modeling in audio source separation)
Daichi Kitamura
•
6 years ago
Factorization Meets the Item Embedding: Regularizing Matrix Factorization with Item Co-occurrence
Dawen Liang
•
7 years ago
Diversity and novelty for recommendation system
Zhenv5
•
11 years ago
More modern gpu
Preferred Networks
•
8 years ago
Deep Learning and Automatic Differentiation from Theano to PyTorch
inside-BigData.com
•
6 years ago
Assurtech : shift technology
Serrerom
•
6 years ago
Utilizing Marginal Net Utility for Recommendation in E-commerce
Liangjie Hong
•
11 years ago
[Rec sys2013勉強会]using maximum coverage to optimize recommendation systems in e commerce
Motoya Wakiyama
•
10 years ago
Recsys 2016: Modeling Contextual Information in Session-Aware Recommender Systems with Neural Networks (Bartłomiej Twardowski)
Bartlomiej Twardowski
•
7 years ago
C++による数値解析の並列化手法
dc1394
•
7 years ago
論文輪読資料「Gated Feedback Recurrent Neural Networks」
kurotaki_weblab
•
8 years ago
Outbrain click prediction by vishalchangrani
Vishal Changrani
•
7 years ago
Outbrain Click Prediction
Alexey Grigorev
•
7 years ago
文章を読み、理解する機能の獲得に向けて-Machine Comprehensionの研究動向-
Takahiro Kubo
•
7 years ago
[AWSマイスターシリーズ] Amazon Elastic MapReduce (EMR)
Amazon Web Services Japan
•
10 years ago
Deep learning を用いた画像から説明文の自動生成に関する研究の紹介
株式会社メタップスホールディングス
•
8 years ago
Heterogeneous Workflows With Spark At Netflix
Jen Aman
•
7 years ago
Learning to understand phrases by embedding the dictionary
Roelof Pieters
•
9 years ago
Improving neural networks by preventing co adaptation of feature detectors
Junya Saito
•
10 years ago
畳み込みニューラルネットワークを用いた複単語表現の解析
奈良先端大 情報科学研究科
•
7 years ago
深層リカレントニューラルネットワークを用いた日本語述語項構造解析
Hiroki Ouchi
•
7 years ago
Personal Information
Organization / Workplace
Within 23 wards, Tokyo, Japan Japan
Occupation
Data Scientist
Industry
Technology / Software / Internet
About
My responsibilities are
- to deliver relevant ads or organic news article for docomo user
- to optimize advertiser's KPI(ROAS, CPA, CVR).
Through these job experiences, I have grown up the following four strong points, ①Service Strategy②Research③Development④Sales and Consulting.
①Service Strategy
Google has so sophiscated Sponsord Search ads, on the other hand we also have the product.
So, we have to think about why advertisers use D2C Sponsord Search ads.
I think the solution is so simple and it is just the more efficient performance compared with the Google product.
To do so, we have to hold some mathematical model to predict or recommend relevant information for many users.
②Researc...
Tags
regret minimization
See more