sibility?
> On Wed, Sep 23, 2015 at 3:02 PM, Demir wrote:
> We've just open sourced a LSH implementation on Spark. We're using this
> internally in order to find topK neighbors after a matrix factorization.
> We hope that this might be of use for others:
> https://github.com/so
use for others:
https://github.com/soundcloud/cosine-lsh-join-spark
For those wondering: lsh is a technique to quickly find most similar
neighbors in a high dimensional space. This is a problem faced whenever
objects are represented as vectors in a high dimensional space e.g. words,
items, us
gt; https://github.com/soundcloud/cosine-lsh-join-spark
>
> For those wondering: lsh is a technique to quickly find most similar
> neighbors in a high dimensional space. This is a problem faced whenever
> objects are represented as vectors in a high dimensional space e.g. words,
We've just open sourced a LSH implementation on Spark. We're using this
internally in order to find topK neighbors after a matrix factorization.
We hope that this might be of use for others:
https://github.com/soundcloud/cosine-lsh-join-spark
For those wondering: lsh is a technique
