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From Sebastian Schelter <...@apache.org>
Subject Re: Hybrid RecSys — ways to do it
Date Mon, 07 Feb 2011 17:50:55 GMT
Hi Alexandre,

I dont think there is "one golden way" but I can give you some hints 
where to start regarding itembased recommenders. I think there are three 
points where you could customize the behavior to enable "hybrid" 
recommendations:

* you can use a custom Rescorer to either filter the resulting 
recommended items (e.g. restrict the result to a certain type/category 
of items) or to boost some of them (e.g. by looking at their content)

* you can use a custom ItemSimilarity which could compute a blended 
score by combining the usual similarity score with an additional 
contentbased similarity score

* as collaborative filtering usually suffers from the "cold-start 
problem" (you cannot make any assumptions about new users or items until 
you've seen some interactions), you could work around this by 
implementing a custom 
CandidateItemsStrategy/MostSimilarItemsCandidateItemsStrategy that uses 
content properties to find items to recommend if the user or the item is 
new


--sebastian

On 07.02.2011 16:56, Alexandre Rodrigues (FEUP) wrote:
> Hello Mahouters out there!
>
> I'm diving into the amazing world of Mahout and Hadoop and I have some
> questions about it. My project consists in developing a recommender system
> for TV shows, and my objective is to study how can I ensemble/mix some
> approaches, like content-based and collaborative filtering (with weights for
> example). Is there _the way_ to do it using Mahout, or it's an unexplored
> subject at the moment?
>
> Thanks in advance!
> --
> Alexandre Rodrigues
>


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