Hi,
I'm an experienced developer yet a complete newbie when it comes to
the type of functionality Mahout offers.
I do have some experience in designing and writing MapReduce jobs in
Hadoop so I understand enough of the base platform that is used.
I want to investigate and experiment with both the item-item and
user-item recommenders in Mahout.
The problem I have is that I'm having a hard time finding a good
overview of the capabilities of the various algorithms.
Most Wikipedia articles immediately dive into the underlying
mathematical foundations instead of the practical implications I'm
looking for.
I've also not been able to find what I'm looking for in the Mahout
Wiki/Confluence.
Putting it simply I'm looking for a comprehensive overview of
- the kind of things you can and cannot do with the various algorithms
that are available in Mahout.
- can it handle both "Implicit" and "Explicit" ratings.
- can I 'age' the relevance of the (implicit) ratings? I.e.
Recommendations should change with the changing taste.
- how does it handle in long tail situations (with millions of
items most are only viewed/rated very infrequently)
- what are the scaling properties of the algorithms.
- is it always batch or can I do real-time incremental updates
with new ratings?
- can I preprocess a daily dataset and then combine the daily sets
into "what I need"?
Thanks for any info you can point me to.
--
Best regards,
Niels Basjes
