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 <sro...@gmail.com> 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 <d99991...@mydit.ie> 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 >
