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.
