Hello Everyone,I was reading through the documentation on the different
itemsimilarity algorithms in mahout and had a question, if one has a scenario
where the number of items are significantly less than the number of users (say
500,000 users to 1000 items) are there particular item similarity coefficients
(namely logLikelihood or tanimoto coeeficient) that lend themself to producing
better recommendations, I've read through the Mahout in action and the java
docs and cant seem to find any clues on this. Any insight based on your
experience would be much appreciated.
Regards
