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：
> I'm new to mapreduce， 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？
>
> --
> Best regards,
> Yin
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