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From Chris Schilling <>
Subject Re: Hybrid RecSys — ways to do it
Date Tue, 08 Feb 2011 00:05:24 GMT
I am interested in this problem as well (combining content similarity with CF).  

I want to build a system which makes use of the CF part of Mahout:  I am recommending products
to users.  Along with user ratings/preferences for products, I also have a content based similarity
metric calculated for each item-item pair.  

I do not have a lot of experience in producing "hybrid" recommendations.  Do you generally
think the most appropriate thing to do is to boost recommendations from CF?  Or do you like
the 2nd method of using a custom item similarity to combine cf similarity with content similarity?
 It seems straight forward enough to try both, just trying to get a feel for how to approach

Can you recommend any papers describing combination of content and CF?

Thanks for your help!
Chris S.

On Feb 7, 2011, at 9:50 AM, Sebastian Schelter wrote:

> 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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