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From yamo93 <yam...@gmail.com>
Subject Re: Need to reduce execution time of RowSimilarityJob
Date Tue, 18 Sep 2012 15:14:03 GMT
Hi Sean,

My need is to compute document similarity (30.000 docs) and more 
precisely, to find the n most similar docs.
As written above, i use RowSimilarityJob but it takes 2h+ to compute.

Seb suggest to use an item-item recommender with input data (term, 
document, tf-idf).

Rgds,
Y.

On 09/18/2012 04:21 PM, Sean Owen wrote:
> If you are computing user-user similarity, the number of items is not
> nearly as important as the number of users. If you have 1M users, then
> computing about 500 billion user-user similarities is going to take a long
> time no matter what.
>
> CSV is the input for both Hadoop-based and non-Hadoop-based
> implementations. The Hadoop-based implementation converts to vectors. You
> can inject vectors directly if you want, there. But you need CSV for the
> non-Hadoop code.
>
> There are a number of tuning params in the Hadoop implementation (and
> similar but different hooks in the non-Hadoop implementation) that let you
> prune data at several stages. This is the most important thing for speed.
> Yes, removing stop-words falls in that category.
>
> Tuning the JVM helps but marginally. More Hadoop nodes helps, linearly.
>
> On Tue, Sep 18, 2012 at 1:49 PM, yamo93 <yamo93@gmail.com> wrote:
>
>> Hi,
>>
>> I have 30.000 items and the computation takes more than 2h on a
>> pseudo-cluster, which is too long in my case.
>>
>> I think of some ways to reduce the execution time of RowSimilarityJob and
>> I wonder if some of you have implemented them and how, or explored other
>> ways.
>> 1. tune the JVM
>> 2. developing an in memory implementation (i.e. without hadoop)
>> 3. reduce the size of the matrix (by removing those which have no words in
>> common, for example)
>> 4. run on real hadoop cluster with several nodes (does anyone have an idea
>> of the number of nodes to make it interesting)
>>
>> Thanks for your help,
>> Yann.
>>


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