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xlvector's solution of NetflixPrize

Xlvector's solution of NetflixPrize. 感谢王元涛同学的帮助，今天实现了RBM模型，用50个hidden unit得到Probe上的RMSE=0.924。将这个结果融合进以前的预测器集合中，得到Quiz上的RMSE从0.8694改进到0.8688。看了RBM确实是一大类方法，能够弥补其他方法的不足。目前我在实验更多hidden unit的RBM，已经用Gauss hidden unit 的RBM。 Http:/ todwang.blogspot.com/2009/04/technical-details-in-rbm-for-cf.html. Http:/ todwang.blogspot.com/2009/03/understanding-rbm-for-cf.html. 因为我用的计算机有4个核，为了充分利用计算机资源，今天将基于SVD的模型重新用多线程进行实现，速度大大提升。 SVD很容易并行化，我的策略是在扫描数据的循环上并行化处理，在修改模型时用锁进行互斥。 Clustering items and users by latent factors?

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xlvector's solution of NetflixPrize | xlnetflixprize.blogspot.com Reviews

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Xlvector's solution of NetflixPrize. 感谢王元涛同学的帮助，今天实现了RBM模型，用50个hidden unit得到Probe上的RMSE=0.924。将这个结果融合进以前的预测器集合中，得到Quiz上的RMSE从0.8694改进到0.8688。看了RBM确实是一大类方法，能够弥补其他方法的不足。目前我在实验更多hidden unit的RBM，已经用Gauss hidden unit 的RBM。 Http:/ todwang.blogspot.com/2009/04/technical-details-in-rbm-for-cf.html. Http:/ todwang.blogspot.com/2009/03/understanding-rbm-for-cf.html. 因为我用的计算机有4个核，为了充分利用计算机资源，今天将基于SVD的模型重新用多线程进行实现，速度大大提升。 SVD很容易并行化，我的策略是在扫描数据的循环上并行化处理，在修改模型时用锁进行互斥。 Clustering items and users by latent factors?

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4 推荐两篇关于rbm的中文介绍

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今天实现了rbm算法,xlvector,标签： rbm,推荐两篇关于rbm的中文介绍,没有评论,程序并行化 multi thread,标签：并行,for example,标签： cluster,标签： knn,关于评分分布的思考,我建立如下的模型来估计他的评分,标签： svd,较旧的帖子,我的博客列表,王元涛的blog,fwd caller info,向量 vector 的空间,pragmatic theory,twitter updates,我的简介,查看我的完整个人资料,博客归档,time,nsvd

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xlvector's solution of NetflixPrize | xlnetflixprize.blogspot.com Reviews

https://xlnetflixprize.blogspot.com

Xlvector's solution of NetflixPrize. 感谢王元涛同学的帮助，今天实现了RBM模型，用50个hidden unit得到Probe上的RMSE=0.924。将这个结果融合进以前的预测器集合中，得到Quiz上的RMSE从0.8694改进到0.8688。看了RBM确实是一大类方法，能够弥补其他方法的不足。目前我在实验更多hidden unit的RBM，已经用Gauss hidden unit 的RBM。 Http:/ todwang.blogspot.com/2009/04/technical-details-in-rbm-for-cf.html. Http:/ todwang.blogspot.com/2009/03/understanding-rbm-for-cf.html. 因为我用的计算机有4个核，为了充分利用计算机资源，今天将基于SVD的模型重新用多线程进行实现，速度大大提升。 SVD很容易并行化，我的策略是在扫描数据的循环上并行化处理，在修改模型时用锁进行互斥。 Clustering items and users by latent factors?

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xlnetflixprize.blogspot.com

1

xlvector's solution of NetflixPrize: An improved item-based KNN predictor

http://xlnetflixprize.blogspot.com/2009/04/improved-item-based-knn-predictor.html

Xlvector's solution of NetflixPrize. An improved item-based KNN predictor. Today, I revise the item-based KNN predictor and get RMSE = 0.8730 in quiz with other 39 predictors. The classical item-based kNN will firstly calculate similarity between item i and j by：. The, the rating r(u,i) will be predicted by:. However, I revise this predictor by:. This predictor can produce more accurate prediction by choosing adequate alpha. 订阅：帖子评论 (Atom). Hector ：go 语言机器学习库. Just a guy in a garage. Follow me on Twitter.

2

xlvector's solution of NetflixPrize: 四月 2009

http://xlnetflixprize.blogspot.com/2009_04_01_archive.html

Xlvector's solution of NetflixPrize. Http:/ todwang.blogspot.com/2009/04/technical-details-in-rbm-for-cf.html. Http:/ todwang.blogspot.com/2009/03/understanding-rbm-for-cf.html. 因为我用的计算机有4个核，为了充分利用计算机资源，今天将基于SVD的模型重新用多线程进行实现，速度大大提升。 SVD很容易并行化，我的策略是在扫描数据的循环上并行化处理，在修改模型时用锁进行互斥。 Clustering items and users by latent factors? By SVD model, we can calculate latent factors for users and items. p(u) is latent factor for u, while q(i) is latent factor for i. S(u,v) = f(p(u), p(v)? 其中，k = m(i). 在预测某个电影的评分时，...

3

xlvector's solution of NetflixPrize: 今天实现了RBM算法

http://xlnetflixprize.blogspot.com/2009/05/rbm.html

Xlvector's solution of NetflixPrize. 感谢王元涛同学的帮助，今天实现了RBM模型，用50个hidden unit得到Probe上的RMSE=0.924。将这个结果融合进以前的预测器集合中，得到Quiz上的RMSE从0.8694改进到0.8688。看了RBM确实是一大类方法，能够弥补其他方法的不足。目前我在实验更多hidden unit的RBM，已经用Gauss hidden unit 的RBM。 Hello happy new year . 愛，拆開來是心和受兩個字。用心去接受對方的一切，用心去愛對方的所有。 . Great msg for me, thanks a lot dude˙﹏˙. 愛，拆開來是心和受兩個字。用心去接受對方的一切，用心去愛對方的所有。 . 你怎麼能經過一片海，而忘記它的藍？ . Thank you for your share . 來幫推你個blog影d相真係好靚,係我至愛 . Yo re-fuel efforts . 成功是一把梯子，雙手插在口袋裡是爬不上去的。 . No pains, no gains. . 中关村, 北京, China.

4

xlvector's solution of NetflixPrize: 推荐两篇关于RBM的中文介绍

http://xlnetflixprize.blogspot.com/2009/04/rbm.html

Xlvector's solution of NetflixPrize. Http:/ todwang.blogspot.com/2009/04/technical-details-in-rbm-for-cf.html. Http:/ todwang.blogspot.com/2009/03/understanding-rbm-for-cf.html. 订阅：帖子评论 (Atom). Hector ：go 语言机器学习库. Just a guy in a garage. Supercomputing made super easy. Netflix Grand Prize technical presentation. Follow me on Twitter. 中关村, 北京, China. Clustering items and users by latent factors? An improved item-based KNN predictor.

5

xlvector's solution of NetflixPrize: clustering items and users by latent factors?

http://xlnetflixprize.blogspot.com/2009/04/clustering-items-and-users-by-latent.html

Xlvector's solution of NetflixPrize. Clustering items and users by latent factors? By SVD model, we can calculate latent factors for users and items. p(u) is latent factor for u, while q(i) is latent factor for i. Recently, I thought about calculate user similarity by latent factor,. S(u,v) = f(p(u), p(v)? I am testing this idea now, and I hope this idea can improve prediction accuracy. 订阅：帖子评论 (Atom). Hector ：go 语言机器学习库. Just a guy in a garage. Supercomputing made super easy. Follow me on Twitter.

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xlnetflixprize.blogspot.com

xlvector's solution of NetflixPrize

Xlvector's solution of NetflixPrize. 感谢王元涛同学的帮助，今天实现了RBM模型，用50个hidden unit得到Probe上的RMSE=0.924。将这个结果融合进以前的预测器集合中，得到Quiz上的RMSE从0.8694改进到0.8688。看了RBM确实是一大类方法，能够弥补其他方法的不足。目前我在实验更多hidden unit的RBM，已经用Gauss hidden unit 的RBM。 Http:/ todwang.blogspot.com/2009/04/technical-details-in-rbm-for-cf.html. Http:/ todwang.blogspot.com/2009/03/understanding-rbm-for-cf.html. 因为我用的计算机有4个核，为了充分利用计算机资源，今天将基于SVD的模型重新用多线程进行实现，速度大大提升。 SVD很容易并行化，我的策略是在扫描数据的循环上并行化处理，在修改模型时用锁进行互斥。 Clustering items and users by latent factors?

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