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Merge pull request apache#460 from srowen/RandomInitialALSVectors
Choose initial user/item vectors uniformly on the unit sphere ...rather than within the unit square to possibly avoid bias in the initial state and improve convergence. The current implementation picks the N vector elements uniformly at random from [0,1). This means they all point into one quadrant of the vector space. As N gets just a little large, the vector tend strongly to point into the "corner", towards (1,1,1...,1). The vectors are not unit vectors either. I suggest choosing the elements as Gaussian ~ N(0,1) and normalizing. This gets you uniform random choices on the unit sphere which is more what's of interest here. It has worked a little better for me in the past. This is pretty minor but wanted to warm up suggesting a few tweaks to ALS. Please excuse my Scala, pretty new to it. Author: Sean Owen <sowen@cloudera.com> == Merge branch commits == commit 492b13a7469e5a4ed7591ee8e56d8bd7570dfab6 Author: Sean Owen <sowen@cloudera.com> Date: Mon Jan 27 08:05:25 2014 +0000 Style: spaces around binary operators commit ce2b5b5a4fefa0356875701f668f01f02ba4d87e Author: Sean Owen <sowen@cloudera.com> Date: Sun Jan 19 22:50:03 2014 +0000 Generate factors with all positive components, per discussion in https://github.com/apache/incubator-spark/pull/460 commit b6f7a8a61643a8209e8bc662e8e81f2d15c710c7 Author: Sean Owen <sowen@cloudera.com> Date: Sat Jan 18 15:54:42 2014 +0000 Choose initial user/item vectors uniformly on the unit sphere rather than within the unit square to possibly avoid bias in the initial state and improve convergence
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