Thanks Sean,
I'm just looking at section "3.3 Coping without preference values" of
Mahout in Action book, that explains how to work with
GenericBooleanPrefDataModel.
On 8 June 2013 14:20, Sean Owen wrote:
> Use an implementation that doesn't expect a rating. These are
> so-called 'boolean' implementations, like GenericBooleanPrefDataModel.
> For example you can build and item-based recommender with the boolean
> version of item based recommender and a log-likelihood similarity.
>
> Or, yes you can calculate some meaningful edge weight to add more info
> to your model. Maybe the number of times the two users interacted? the
> resulting number can be used as a 'rating' although I don't know if
> you will get great results since it doesn't act a lot like a rating.
> Instead, use the log of this number.
>
> Or, use an algorithm that is comfortable with count-like input, like
> ALS with the "implicit data" option turned on.
>
> Sean
>
> On Sat, Jun 8, 2013 at 2:15 PM, Peter Holland wrote:
> > Hi All,
> > I am trying to use Mahout for Link Prediction in a Social Network.
> >
> > The data I have is an edges list with 9.4 million rows. The edge list is
> a
> > csv vile where each node is an integer value and a row represents a edge
> > between two nodes. For example;
> >
> > 3432, 5098
> > 3423, 6710
> > 4490, 5843
> > 4490, 2039
> > .
> >
> > This is a directed graph so row 1 means that node 3432 follows node 5098.
> >
> > I would like to build a recommender to calculate the top 10 nodes a user
> > might like to connect to next. The problem I have is that the recommender
> > classes needs input in the form (user, item, value). So, how can I first
> > calculate a value to represent the 'weight' of an edge? For example
> > EdgeRank?
> >
> > Any help would be greatly appreciated.
> > Thank you,
> > Peter
>