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 <[email protected]> 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
