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 <[email protected]>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 <[email protected]> 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 <[email protected]>
> > 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
>
