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From Frederik Kraus <>
Subject (near) real time recommender/predictor
Date Thu, 31 Jan 2013 19:02:13 GMT
Hi Guys,  

I'm rather new to the whole Mahout ecosystem, so please excuse if the questions I have are
rather dumb ;)

Our "problem" basically boils down to this: we want to match users with either the content
they interested in and/or the content they could contribute to. To do this "matching" we have
several dimensions both of users and content items (things like: contribution history, tags,
browsing history, diggs, likes, ….).

As interest of users can change over time some kind of CF algorithm including temporal effects
would obviously be best, but for the time being those effects could probably be neglected.

Now my questions:

- what algorithm from the mahout "toolkit" would best fit our case?
- How can we get this near realtime, i.e. not having to recalculate the entire model when
user dimensions change and/or new content is being added to the system (or updated)
- how would we model the user and item vectors (especially things like "tags")?
- any hints on where to start? ;)

Thanks a lot!


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