mahout-user mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From Steven Bourke <sbou...@gmail.com>
Subject Re: Hybrid RecSys — ways to do it
Date Tue, 08 Feb 2011 00:11:28 GMT
Check http://www.springerlink.com/content/n881136032u8k111/ out. Do a search
on google scholar and you might find  the pdf.

What type of data / recommendations are you trying to make? Standard
collaborative filtering techniques arent a bad thing.

On Tue, Feb 8, 2011 at 12:05 AM, Chris Schilling <chris@cellixis.com> wrote:

> 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 this.
>
> 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
> >>
> >
>
>

Mime
  • Unnamed multipart/alternative (inline, None, 0 bytes)
View raw message