No it is definitely not true that you'll get the same result from a user-based and item-based recommender, even with the same similarity metric. They're different algorithms, but are actually purposely asymmetric too to take advantage of the difference, in practice, between what a user is and what an item is.
Tanimoto and LL are different, yes. I suppose it's possible you will get the same recommendations with both, especially on a small toy data set. But no it's not true that they would always give the same result. On Sat, Apr 23, 2011 at 3:41 AM, Otis Gospodnetic < [email protected]> wrote: > Hi, > > Given the same input data, should the same list of recommended items be > returned > regardless of whether one uses Item-based or User-based recommendations? I > always thought the answer was yes (same "matrix" just flipped differently > is how > I imagined it), but I recently saw output of some Mahout-based recommender > that > returned two different lists of recommendations based on whether User-based > of > Item-based approach was used. Either the code was buggy or I was wrong. :) > > And while I'm at it, I assume that using Tanimoto vs. LogLikelihood will > yield > different recommendations, right? Again, I'm asking because I saw some > Mahout-based recommender recently that used Item-based approach and > returned > identical lists for both Tanimoto and LogLikelihood. > > Let: > UB stand for User-based > IB stand for Item-based > TC stand for TanimotoCoefficient > LL stand for LogLikelihood > > And: > R1 = UB with TC > R2 = UB with LL > R3 = IT with TC > R4 = IT with LL > > Then: > R1 != R2 <== ? > R3 != R4 <== ? > > And: > R1 == R3 <== ? > R2 == R4 <== ? > > Thanks, > Otis > -- > We're hiring Mahout+HBase hackers for Data Mining and Analytics > > http://blog.sematext.com/2011/04/18/hiring-data-mining-analytics-machine-learning-hackers/ >
