On Fri, Mar 23, 2012 at 8:33 PM, Ahmed Abdeen Hamed <[email protected]> wrote: > As for merging the scores, I need an OR rule, which translates to the > addition. If I used AND that will make the likelihood smaller because the > probabilities will be multiplied. This will restrict the clusters to items > that appears in the intersection of content-based similarity AND sales > correlations. Does this sound right to you?
Not really, because of course you multiply probabilities in all cases. Yes, all similarities would be smaller in absolute term, but that's fine -- the absolute value does not matter. The problem with adding is that again it assumes the two terms are in the same "units" and that is not clear here. The product doesn't contain that assumption, at least. > > A very important issue I am having now is about evaluation. How do we > evaluate these clusters resulting from a TreeClusteringRecommender? > In the context of recommenders, you don't. The clusters are not the output, just a possible implementation by-product. You could compute metrics like intra-cluster distance vs inter-cluster distance but I don't know what it says about the quality of the recs. You should start with the standard rec eval code if you can.
