Hi Ted; I don't have constraint, I have to compute all the distances, but the distances are already computed, I already have a text file which tells me the pairwise distances among all the users and I need to fill the mahout user-based algo with these distances.
Hi Pat, I don't understand why it is not a Mahout problem, my goal is to evaluate (RMSE) the output of a user based algorithm comparing different user similarity measures, Mahout already has everything I need except the fact I cannot give in input a custom similarity matrix. Eugenio 2015-02-13 21:51 GMT+01:00 Pat Ferrel <[email protected]>: > If the user -> similar users relationship is really fixed for some test > this isn’t even a Mahout problem… All you need to do is create a linear > combination of all the similar user's preferences and rank accordingly. > This produces ranked recs for some “current user”. If you have a record of > user preferences and similar users it’s not even a Mahout thing. A DB will > do this just fine for a test. > > The current code in spark-rowsimilarity will give similar users based on > interaction input data using LLR. Adding a custom distance metric to > SimilarityAnalysis.rowSimilarity should be pretty easy. > > So you have several ways to go using new code or old Taste code. To make > it work generally you’ll have to write some code since your metric is > really new. > > > On Feb 13, 2015, at 11:14 AM, Ted Dunning <[email protected]> wrote: > > On Fri, Feb 13, 2015 at 11:11 AM, Eugenio Tacchini < > [email protected]> wrote: > > > Is there anyone who can give me some hints about this task? > > > > Another way to look at this is to try to wedge this into the item > similarity code. > > There are hooks available in the map-reduce version of item similarity to > put an arbitrary user distance in. This only works well if there are > sparsity constraints that limit the number of distances that need to be > computed, but if it works, it can be really excellent. This would allow > you to put your distances in and still use an indicator-based recommender. > >
