WEB+DB press vol.49にレコメンド特集の記事をtkngさんと書きました。 内容は最初は、協調フィルタリングやコンテンツマッチの簡単な話から、特徴量をどのように表すか、大規模データをどのように処理するかにいき、特異値分解などの低ランク行列分解によるレコメンドやRestricted Boltzmann Machineといった最近のnetflix prizeの上位の手法など、かなり突っ込んだ議論もしてます。 個人的には三章でLocality Sensitive Hash(LSH)について扱っているあたりがお勧めです。 レコメンドの内部の問題を極言すると、データというのは疎な高次元の数値ベクトル(数百万次元とか)で表され、クエリでベクトルが与えられた時、これと似たようなベクトルを探してこいという問題になります。”似たような”を数学的にいえば、クエリのベクトルとの内積(各ベクトルは長
Restricted Boltzmann Machines for Collaborative Filtering Ruslan Salakhutdinov rsalakhu@cs.toronto.edu Andriy Mnih amnih@cs.toronto.edu Geoffrey Hinton hinton@cs.toronto.edu University of Toronto, 6 King’s College Rd., Toronto, Ontario M5S 3G4, Canada Abstract Most of the existing approaches to collab- orative filtering cannot handle very large data sets. In this paper we show how a class of two-la
The best paper award at the recent KDD 2009 conference went to Yehuda Koren's "Collaborative Filtering with Temporal Dynamics" (PDF). The paper is a great read, not only because Yehuda is part of the team currently winning the Netflix Prize, but also because it has some surprising conclusions about how to deal with changing preferences and interests over time. In particular, it is common in recomm
"The way I rate movies today can be very different from how I rate them even tomorrow," says Yehuda Koren of Yahoo! Research Israel. Those differences are accounted for by the improved algorithm that makes Koren's team the likely winner of the NetFlix Pri When the organizers of the Netflix Prize contest announced late last week that one team had met the requirement for the $1 million Grand Prize,
When the organizers of the Netflix Prize contest announced late last week that one team had met the requirement for the $1 million Grand Prize, Yehuda Koren, a member of the seven-person multinational team, was in Paris to present a paper at KDD-09, the 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. The ideas he laid out won the conference's Best Paper Award — and, not coincide
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