August 2015
Intermediate to advanced
312 pages
7h 28m
Japanese
Content preview from 初めてのSpark
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241
11.4 データ型
RDD
の各要素には、ユーザーの
ID
、商品の
ID
、そして評価（これは明示的な評価でも、暗黙の
フィードバックでもかまいません。この後の議論を参照してください）。この実装における課題の
1
つは、それぞれの
ID
が
32bit
の整数でなければならないことです。もしも使用する
ID
が文字列
であったり、
32bit
に収まらないほど大きい場合には、
ALS
では各
ID
のハッシュコードを使えば
良いでしょう。
2
人のユーザー、あるいは
2
つの製品が同じ
ID
にマップされたとしても、全体と
して得られる結果の質は同等になり得ます。あるいは、
broadcast()
を使って製品
ID
と整数値の
マッピングのテーブルをブロードキャストし、それぞれの製品にユニークな
ID
を与えることもで
きます。
ALS
は、学習の結果を表す
MatrixFactorizationModel
を返します。
MatrixFactorizationModel
の
predict()
を使えば、（
userID, productID)
というペアからなる
RDD
の評価を予測できま
す
†
。あるいは、
model.recommendProducts(userId, numProducts)
を使って、あるユーザーに
対する商品の上位のレコメンデーションを
numProducts()
の数だけ得ることもできます。た
だし注意が必要なのは、
MLlib
ーー
1
MatrixFactorizationModel
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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.