特異値分解を用いたレコメンデーション - NO!と言えるようになりたい
Recommender Systems 2007(http://recsys.acm.org/2007/)で発表された論文である，Bhaskar Mehta, Thomas Hofmann, and Wolfgang Nejdl, Robust Collaborative Filtering, In Proceedings of the 1st ACM Conference on Recommender Systems, ACM Press, October 2007, pp. 49–56. を読んだメモです．この論文では，ある種の攻撃に耐えられるような，頑強な協調フィルタリングの手法を提案していますが，その説明は後日行うことにして，今回は，関連研究に挙げられていた，特異値分解を用いたレコメンデーションアルゴリズムについて説明を行いたいと思います． 特異値分解 ある任意のm x n行列は下
Algorithms :: Single Value Decomposition
Dimensionality Reduction and the Singular Value Decomposition Dimensionality Reduction One common way to represent datasets is as vectors in a feature space. For example, if we let each dimension be a movie, then we can represent users as points. Though we cannot visualize this in more than three dimensions, the idea works for any number of dimensions. One natural question to ask in this setting i
SVDLIBC
SVDLIBC is a C library based on the SVDPACKC library, which was written by Michael Berry, Theresa Do, Gavin O'Brien, Vijay Krishna and Sowmini Varadhan. SVDLIBC offers a cleaned-up version of the code with a sane library interface and a front-end executable that performs matrix file type conversions, along with computing singular value decompositions. Currently the only SVDPACKC algorithm implemen
SVD Recommendation System in Ruby - igvita.com
By Ilya Grigorik on January 15, 2007 One day, a bunch of friends, who happened to be big Family Guy fans, decided to put together a site to rank and share their thoughts on the show. Soon thereafter they had a Rails site up and running, and all was well, and other fans joined in hordes. A web 2.0 success! Then one day they realized that they could no longer track everyone's ratings, their user-bas
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