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From Chris Bates <christopher.andrew.ba...@gmail.com>
Subject Re: recommendations
Date Mon, 30 Aug 2010 15:20:01 GMT
I really need to take a look at the Mahout code (I haven't had a chance yet)
so I'm not sure if this type of rec is possible, but what I would do is
something like this:

Enumeration of Search Terms:
1      Bath soap
2      Headphones
3      Computer laptop

Enumeration of Users:
userid 1
userid 2
userid 3
userid 4

Joined Matrix
UserID      TotalCountOfSearchItem      SearchItemID
LocalCountSearchItem
1                               15                                     1
                            3
1                                7                                      2
                              1
2                                15                                    1
                            5
2                                7                                      2
                              4
3                                10                                    3
                            5
3                                15                                    1
                            7
4                                10                                    3
                            5
4                                7                                      2
                              2

I wrote a blog post about Naive Bayes for classification tasks that
describes this type of layout here:

http://www.thedatascientist.com/2010/05/22/how-i-would-use-the-google-prediction-api/

But this type of data layout is algorithm agnostic, so you can use it for
whatever you need to do.  Its just a matter of feeding the data into a form
that Mahout will recognize (my guess)

Chris


On Mon, Aug 30, 2010 at 10:47 AM, Pramit Vamsi <pramit.vamsi@gmail.com>wrote:

> I have some understanding now. So given 2 matrices user * (page view/search
> term) and user * (purchased item), how do you connect these 2 matrices
> given that I can define the user or item sim methods?
>
> Also, can the second use case can be solved with CF or association mining
> is
> needed?
>
> Pramit
>
> On Mon, Aug 30, 2010 at 12:07 AM, Sean Owen <srowen@gmail.com> wrote:
>
> > Yes, this is a simpler problem. You just want to find which items are
> > most similar to a given item, for some definition of 'similar'.
> > GenericItemBasedRecommender has a mostSimilarItems() method that just
> > saves you the trouble of computing this by hand, and any
> > ItemSimiliarity function you like can be used.
> >
> > On Sun, Aug 29, 2010 at 7:26 PM, Ted Dunning <ted.dunning@gmail.com>
> > wrote:
> > > These are examples of what I call cross-recommendation where you have
> > user x
> > > item1 and user x item2 data and you
> > > want item1 => item2 recommendations.
> > >
> > > All of the standard techniques apply (user-based, LLR cooccurrence,
> SVD,
> > > latent factor models), but you have to rejigger things here
> > > and there.
> > >
> > > Sean, can Mahout's recommendation system do this cross recommendation?
> > >
> >
>
>
>
> --
> Thanks,
> Pramit
>

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