(I think this is the same question you just asked on [email protected]? Copying my answer.)
You may not need Hadoop at all, and if you don't, I'd suggest you not use it for simplicity. It's "necessary evil" to scale past a certain point.
You can have data on Cassandra but you will want to be able to read it into memory. If you can dump as a file, you can use FileDataModel. Or, you can emulate the code in FileDataModel to create one based on Cassandra.
Then, your two needs are easily answered:
This is not even a recommendation
problem. Just pick an implementation
of UserSimilarity, and use it to
compare a user to all others, and
pick the ones with highest
similarity. (Wrapping with
CachingUserSimilarity will help a
lot.)
This is just a recommender
problem. Use a
GenericUserBasedRecommender with
your UserSimilarity and DataModel
and you're done.
It of course can get much more complex than this, but this is a fine start point.
If later you use Hadoop, yes you have to set up Hadoop according to its instructions. There is no Mahout "setup". For recommenders, you would look at one of the RecommenderJob classes which invokes the necessary jobs on your Hadoop cluster. You would run it with the "hadoop" command -- again, this is where you'd need to just understand Hadoop.
The book Mahout in Action writes up most of the Mahout Hadoop jobs in some detail.