October 2014
Intermediate to advanced
288 pages
6h 27m
Japanese
Content preview from 実践 機械学習システム
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162
8
章 回帰：レコメンドの改良
# user
のレーティングを二値化する
bu = user > 0
# rest
のレーティングを二値化する
br = rest > 0
ws = all_correlations(bu,br)
#
相関係数の大きい順に
100
個インデックスを選ぶ
selected = ws.argsort()[-100:]
#
平均値を基に予測する
estimates = rest[selected].mean(0)
#
ある映画は他の映画より多くのユーザから点数を付けられている
#
それを考慮して、予測結果を修正する必要がある
estimates /= (.1+br[selected].mean(0))
ここでは対象ユーザに対してそのユーザと似ている100人のユーザを選び出し、その選んだユーザ
のスコアの平均値を予測結果としています。こ
れはデータセットの全ユーザを用いて予測した場合と
比較すると、RMSEを20％減らすことができます。いつものように、映画の点数を付ける回数が多け
れば多いほど、予測の精度は良くなります。映画の点数を付ける回数の多い上位半分のユーザを対象
にすると、25％誤差を減らすことができます。
8.1.2
類似映画について考える
前の節では、「類似ユーザ」について考えました。それと同様に、「類似映画」についても考えること
ができます。ここでは、映画を対象とした最近傍探索に基づく推薦システムを作成することにします。
ここで、ユーザUの映画Mに対するレーテ
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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.