July 2014
Beginner to intermediate
456 pages
11h 26m
Japanese
Content preview from 戦略的データサイエンス入門 ―ビジネスに活かすコンセプトとテクニック
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342
12 章 その他のデータサイエンスの問題と技法
グによって、
1.
どれだけ映画が奇妙で
2.
視聴者が奇妙な映画をどれだけ好きか、を同時に決
定している、ということです。
ここでもまた、目的関数を定義して、どのモデルがよくフィットしているかを決める必要
があります。そのため、いままでに集計された映画視聴率のデータ一式をもとにして、ト
レーニングのための目的関数を定義します。そして、この次元に従って顧客と映画を特徴付
ける重みのセットを見つけ出します。映画のレコメンデーション問題においては、さまざま
な目的関数が利用できます。例えば、重みを選ぶときに、トレーニングデータに見られた映
画の評価を最もよく予測できるようにする選び方があります（
4
章で説明したモデルの調整
の話を思い出しましょう）。あるいは、次元を選ぶときに、集計された多様な視聴率を最も
的確に説明できるものを採用するやり方もあります。これはよく「
Matrix Factorization
」
と呼ばれます。興味のある方は、まずは
Netflix Challenge
の論文［
Koren, Bell, & Volinsky
2009
］を見ると良いでしょう。
結果として、ある程度減らした次元の上で、映画を表現する方法を手にしたことになりま
す。その次元は、例えば、どれだけ奇妙か、「感動もの」か、「男性向け」か、といったもの
で、トレーニングによって見つけた最適な
d
の潜在的次元であれば何でも良いのです。ま
た、これらの次元の上で、それぞれのユーザの好みを表現する方法も同時に手に入れていま ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.