My first post to the Mahout group. First, Mahout devs, you have created something great so thanks!
I have inherited some Mahout code and am trying to make some improvements. I was hoping to get some guidance. 1. We are using the NearestNUserNeighborhood class for neighborhood calculations. While I want to use the similarity metrics provided in Mahout, I also want to introduce some randomness. In effect, I want to include a few people into the final nearest neighbors set that are not actually that close. That way, my recommender will include some outliers into the results which is a desirable property for our recommender. What's the best way of doing this? I can of course implement my own similarity metric (which could internally use PearsonCorrelationSimilarity) and then randomly give a high correlation number to certain people. But is there a better way? 2. I also want to introduce some randomness into the final recommended set. I am thinking I can do this by creating a custom IDRescorer and randomly bumping up the score for some of the items. This will of course require some tweaking (how often an item gets a bump, how much of a bump does it get, etc.) but is there a better way of going about this? Thanks for the help! -Salil
