As clarification, here are the relevant papers. The approach for explicit feedback [1] does not use unobserved cells, only the approch for handling implicit feedback [2] does, but weighs them down.
/s [1] "Large-scale Parallel Collaborative Filtering for the Netflix Prize" http://www.hpl.hp.com/personal/Robert_Schreiber/papers/2008%20AAIM%20Netflix/netflix_aaim08(submitted).pdf [2] "Collaborative Filtering for Implicit Feedback Datasets" http://research.yahoo.com/pub/2433 On 25.03.2013 14:51, Dmitriy Lyubimov wrote: > On Mar 25, 2013 6:44 AM, "Sean Owen" <sro...@gmail.com> wrote: >> >> (The unobserved entries are still in the loss function, just with low >> weight. They are also in the system of equations you are solving for.) > > Not in the classic alswr paper i was specifically referring to. It actually > uses minors of observations with unobserved rows or columns thrown out. > The solution you are often referring to, the implicit feedback, indeed does > not since it is using a different observation encoding technique. > >> >> On Mon, Mar 25, 2013 at 1:38 PM, Dmitriy Lyubimov <dlie...@gmail.com> > wrote: >>> Classic als wr is bypassing underlearning problem by cutting out unrated >>> entries from linear equations system. It also still has a fery defined >>> regularization technique which allows to find optimal fit in theory (but >>> still not in mahout, not without at least some additional sweat, i > heard). >
