May 2020
Intermediate to advanced
456 pages
9h 13m
Japanese
Content preview from ゼロからはじめるデータサイエンス 第2版 ―Pythonで学ぶ基本と実践
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23.5
行列分解
■
361
23.5
行列分解
これまで見てきたように、ユーザの興味を
1
と
0
の
[num_users,
num_items]
行列
として表しました。
1
は興味のある項目、
0
は興味のない項目を表します。
実際、数値の評価（
Rating
）を行うことがあります。例えば、
Amazon
のレビューを
書くときは、
1
〜
5
個の範囲で星を割り当てます。これらを
[num_users,
num_items]
行列で表してみましょう（現時点では、未評価の項目をどう表すかという問題は無視し
ま す ）。
このセクションでは、このような評価データがあると仮定し、特定のユーザと項目
の評価を予測できるモデルを学習します。
この問題に対する解の
1
つは、すべてのユーザが数字のベクトルとして表現できる
潜在的なタイプを持ち、各項目も同様に潜在的なタイプを持つと仮定することです。
ユーザのタイプが
[num_users,
dim]
行列として表され、アイテムのタイプの転置
が
[dim,
num_items]
行列として表される場合、それらの積は
[num_users,
num_
items]
行列となります。したがって、このようなモデルを作成する
1
つの方法は、興
味行列をユーザ行列とアイテム行列の積に分解することです。
（おそらくこの潜在タイプの考え方は、「21章 自然言語処理」で開発した単語の埋
め込みを思い出させます。その考えを、そのまま持っておいてください。）
10
人の興味を集めたデータセットではなく、
MovieLens
の
100k
デ
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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.