The "abstract information structure" encoded in the item-item graph is completely different from the user-user graph. Also, there are different User-based and Item-based approaches. Comparing recommendations is hard. It is not really possible to make an absolute or even fuzzy ranking of "what should be recommended".
Lance On Fri, Apr 22, 2011 at 7:41 PM, 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/ > -- Lance Norskog [email protected]
