Case 1 is fine, in case 2, I don't think that a dot product (without
normalization) will yield a meaningful distance measure. Cosine
distance or a Pearson correlation would be better. The situation is
similar to Latent Semantic Indexing in which documents are represented
by their low rank
On Sat, Jan 25, 2014 at 3:51 PM, Tevfik Aytekin tevfik.ayte...@gmail.comwrote:
Case 1 is fine, in case 2, I don't think that a dot product (without
normalization) will yield a meaningful distance measure. Cosine
distance or a Pearson correlation would be better. The situation is
similar to
Hi Ted,
Could you explain what do you mean by a dithering step and an
anti-flood step?
By dithering I guess you mean adding some sort of noise in order not
to show the same results every time.
But I have no clue about the anti-flood step.
Tevfik
On Sat, Jan 25, 2014 at 11:05 PM, Koobas
Dithering is commonly done by re-ranking results using a noisy score. Take
r to be the original rank (starting with 1). Then compute a score as
s = log r + N(0,log \epsilon)
and sort by this new score in ascending order.
Items will be shuffled by this method in such a way that the
For anti-flood and in the vein of “UI” you can build a recommender that
recommends categories or genres then get recommendations weighted or filtered
by those categories. A simple version of this is to just look at preference
frequency by category for the current user. This is a lot like what
On Sat, Jan 25, 2014 at 4:33 PM, Pat Ferrel p...@occamsmachete.com wrote:
BTW can you explain your notation? s = log r + N(0,log \epsilon)
N?, \epsilon?
r is rank
N is normal distribution
\epsilon is an arbitrary constant that drives the amount of mixing.
Typical values are =4.
N(0, log\epsilon) = Normal Distribution with Mean = 0 and Variance =
log(epsilon)
On Saturday, January 25, 2014 7:33 PM, Pat Ferrel p...@occamsmachete.com
wrote:
For anti-flood and in the vein of “UI” you can build a recommender that
recommends categories or genres then get
