On Wed, Jun 9, 2010 at 11:19 AM, Richard Simon Just <
info@richardsimonjust.co.uk> wrote:
>
> I don't know enough yet to comment on what works best, but I can give some
> evidence that they do subtract teh row average ahead of time. Sarwar's
> previous work, Application of Dimensionality Reduction piece (
> http://www.grouplens.org/papers/pdf/webKDD00.pdf) uses the same prediction
> function. In section 4.3.1 Prediction Experiment they discuss the removal of
> the row average before the SVD computation and it's later addition for the
> prediction. I'd make the assumption that the incremental SVD paper builds on
> this.
>
> - Richard
>
I would be *very* careful on how you decompose a sparse matrix which you
center: if you naively just subtract off the mean from all the entries in
the vectors, an SVD which would have taken 6 hours to compute could suddenly
take weeks, literally. But if you do the second-from-most-naive thing, and
subtract the means from only the nonzero entries, then all can turn out for
the best. This is just following Sean's typical advice of "don't treat
unknown preferences as '0.0' ".
-jake