I just meant that you can make recommendations with the data you have, without using Mahout. But I see now that you are trying to use it to calculate RMSE. And that requires Taste. I believe using it has already been described below.
It should be noted that, except for a few special cases, RMSE is not longer considered a very good test of recommenders. Unless you are really trying to predict ratings it is not useful. If you want to optimize _ranking_, in other words you want to show the best n recommendations, you want a precision metric like MAP (mean average precision). MAP is not built into Mahout of any flavor. It should also be noted that doing offline comparison of different algorithms is fraught with problems and so should only be undertaken with a good degree of skepticism. On Feb 14, 2015, at 6:05 AM, Eugenio Tacchini <eugenio.tacch...@gmail.com> wrote: 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 <p...@occamsmachete.com>: > 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 <ted.dunn...@gmail.com> wrote: > > On Fri, Feb 13, 2015 at 11:11 AM, Eugenio Tacchini < > eugenio.tacch...@gmail.com> 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. > >
