Thanks, I think the small value idea is probably on the right track. I
should clarify that I've written a Recommender that uses the
clustering-on-Hadoop bits of Mahout, and in doing so I explicitly map the
DataModel preference vectors, which distinguish 0 from null as you say, to
RandomAccessSparseVectors, which don't. So at that point I just extend the
mapping to go from 0.0 to a small number (MIN_VALUE is actually too small
for me, since my distance metric computes ends up calculating MIN_VALUE *
MIN_VALUE, which of course zeroes out, which I don't want).
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