The document provides an introduction and background about the speaker, Kenichi Matsui. It discusses his career experience working for several large companies in software development, communications, and consulting. It then covers some of his current responsibilities related to data analysis and machine learning as a data scientist and group manager. Specific topics covered include an overview of data science skills and roles, machine learning techniques like classification and regression, and data analysis competitions.
The document provides an introduction and background about the speaker, Kenichi Matsui. It discusses his career experience working for several large companies in software development, communications, and consulting. It then covers some of his current responsibilities related to data analysis and machine learning as a data scientist and group manager. Specific topics covered include an overview of data science skills and roles, machine learning techniques like classification and regression, and data analysis competitions.
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User Discovery User data
Pre-process
Feature
Model training
Trained model
Prediction
p_click
data
1. Pre-process
1.1 配信データから、各ユーザーごとに特徴量(ウェブ
行動履歴)を抽出
1.2 学習用データ(Clickの有り無し)をラベル付け
2. モデルの学習
1で加工した学習データを用いて、MLP (multi layer
perceptron)モデルを学習
3. 予測
3.1 1で加工した予測用データを、2で学習したモデルに
インプットして、クリック確率(のようなもの)を計算
3.2 user x p_clickテーブルを作成
3.3 このうち、p_clickの高いUserを配信に用いる