Hey Sebastian,
Thanks again for the explanation. So now you have me intrigued about
something else. Why is it that logliklihood ratio test is a better measure
for essentially implicit ratings? Are there resources/research papers you
can point me to explaining this?
Take care
Amit
On Sun, Dec 1, 2013 at 9:25 AM, Sebastian Schelter
wrote:
> Hi Amit,
>
> No need to excuse for picking on me, I'm happy about anyone digging into
> the paper :)
>
> The reason, I implemented Pearson in this (flawed) way has to do with
> the way the parallel algorithm works:
>
> It never compares two item vectors in memory, instead it preprocesses
> the vectors and computes sparse dot products in parallel. The centering
> which is usually done for Pearson correlation is dependent on which pair
> of vectors you're currently looking at (and doesn't fit the parallel
> algorithm). We had an earlier implementation that didn't have this flaw,
> but was way slower than the current one.
>
> Rating prediction on explicit feedback data like ratings for which
> Pearson correlation is mostly used in CF, is a rather academic topic and
> in science there are nearly no datasets that really require you to go to
> Hadoop.
>
> On the other hand item prediction on implicit feedback data (like
> clicks) is the common scenario in the majority of industry usecases, but
> here count-based similarity measures like the loglikelihood ratio test
> give much better results. The current implementation of Mahout's
> distributed itembased recommender is clearly designed and tuned for the
> latter usecase.
>
> I hope that answers your question.
>
> --sebastian
>
> On 01.12.2013 18:10, Amit Nithian wrote:
> > Thanks guys! So the real question is not so much what's the average of
> the
> > vector with the missing rating (although yes that was a question) but
> > what's the average of the vector with all the ratings specified but the
> > second rating that is not shared with the first user:
> > [5 - 4] vs [4 5 2].
> >
> > If we agree that the first is 4.5 then is the second one 11/3 or 3
> > ((4+2)/2)? Taste has this as ((4+2)/2) while distributed mode has it as
> > 11/3.
> >
> > Since Taste (and Lenskit) is sequential, it can (and will only) look at
> > co-occurring ratings whereas the Hadoop implementation doesn't. The paper
> > that Sebastian wrote has a pre-processing step where (for Pearson) you
> > subtract each element of an item-rating vector from the average rating
> > which implies that each item-rating vector is treated independently of
> each
> > other whereas in the sequential/non-distributed mode it's all considered
> > together.
> >
> > My main reason for posting is because the Taste implementation of
> item-item
> > similarity differs from the distributed implementation. Since I am
> totally
> > new to this space and these similarities I wanted to understand if there
> is
> > a reason for this difference and whether or not it matters. Sounds like
> > from the discussion it doesn't matter but understanding why helps me
> > explain this to others.
> >
> > My guess (and I'm glad Sebastian is on this list so he can help
> > confirm/deny this.. sorry I'm not picking on you just happy to be able to
> > talk to you about your good paper) is that considering co-occuring
> ratings
> > in a distributed implementation would require access to the full matrix
> > which defeats the parallel nature of computing item-item similarity?
> >
> > Thanks again!
> > Amit
> >
> >
> > On Sun, Dec 1, 2013 at 2:55 AM, Sean Owen wrote:
> >
> >> It's not an issue of how to be careful with sparsity and subtracting
> >> means, although that's a valuable point in itself. The question is
> >> what the mean is supposed to be.
> >>
> >> You can't think of missing ratings as 0 in general, and the example
> >> here shows why: you're acting as if most movies are hated. Instead
> >> they are excluded from the computation entirely.
> >>
> >> m_x should be 4.5 in the example here. That's consistent with
> >> literature and the other implementations earlier in this project.
> >>
> >> I don't know the Hadoop implementation well enough, and wasn't sure
> >> from the comments above, whether it does end up behaving as if it's
> >> "4.5" or "3". If it's not 4.5 I would call that a bug. Items that
> >> aren't co-rated can't meaningfully be included in this computation.
> >>
> >>
> >> On Sun, Dec 1, 2013 at 8:29 AM, Ted Dunning
> wrote:
> >>> Good point Amit.
> >>>
> >>> Not sure how much this matters. It may be that
> >>> PearsonCorrelationSimilarity is bad name that should be
> >>> PearonInspiredCorrelationSimilarity. My guess is that this
> >> implementation
> >>> is lifted directly from the very early recommendation literature and is
> >>> reflective of the way that it was used back then.
> >>
> >
>
>