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MapReduce >> mail # user >> How can I build a Collaborative Filtering recommendation framework based on mapreduce


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Re: How can I build a Collaborative Filtering recommendation framework based on mapreduce
Mahout is built precisely, so I think that you can evaluate it again.
It has to two collaborating filtering algorithms:

- Non-distributed recommenders ("Taste")
https://cwiki.apache.org/confluence/display/MAHOUT/Recommender+Documentation

- Distributed recommenders ("Item-based")
https://cwiki.apache.org/confluence/display/MAHOUT/Itembased+Collaborative+Filtering

- First-time FAQSs
https://cwiki.apache.org/confluence/display/MAHOUT/Recommender+First-Timer+FAQ

About the test that you did with Mahout:
- Which are the features of your machine?
If you are working with 175M of data, a single machine
is not the best way to do it. It's more worthy if you use
small Hadoop cluster for this (1 NN/JT and 3 DN/TT), and then
you can ask on the Mahout mailing list how to improve the performance
of your system.

Regards

On 3/31/2012 6:17 AM, chao yin wrote:
> Hi all锟斤拷
> I'm new to mapreduce锟斤拷 but familiar with Collaborative Filtering
> recommendation framework.
> I tried to use mahout to do this work. But it disappointed me. My
> machine work all day to do this job without any result with about 175M data.
> Is there anyone knows anything about Collaborative Filtering
> recommendation framework based on mapreduce, or mahout, any suggestion
> to improve performance锟斤拷
>
> --
> Best regards,
> Yin

--
Marcos Luis Ort锟斤拷z Valmaseda (@marcosluis2186)
 Data Engineer at UCI
 http://marcosluis2186.posterous.com

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NEW: Monitor These Apps!
elasticsearch, apache solr, apache hbase, hadoop, redis, casssandra, amazon cloudwatch, mysql, memcached, apache kafka, apache zookeeper, apache storm, ubuntu, centOS, red hat, debian, puppet labs, java, senseiDB