> The affect of downweighting the popular items is very similar to
> removing them from recommendations so I still suspect precision will
> go down using IDF. Obviously this can pretty easily be tested, I just
> wondered if anyone had already done it.
>
> This brings up a problem with holdout bas
The affect of downweighting the popular items is very similar to removing them
from recommendations so I still suspect precision will go down using IDF.
Obviously this can pretty easily be tested, I just wondered if anyone had
already done it.
This brings up a problem with holdout based precisi
This results in no information for universally preferred items, which
is indeed what I was looking for. It looks like this should also work
for other values or explicit preferences--item prices, ratings,
etc..
Intuition says this will result in a lower precision related cross
validation measu
oops, forgot the log
So...
idf weighted preference value = item preference value * log (number of all
items/number of users with specific item pref)
items
1 0 0
users 1 0 0
1 1 0
freq
On Tue, Feb 5, 2013 at 11:29 AM, Pat Ferrel wrote:
> I think you meant: "Human relatedness decays much slower than item
> popularity."
>
Yes. Oops.
> So to make sure I understand the implications of using IDF… For
> boolean/implicit preferences the sum of all prefs (after weighting) for a
>
I think you meant: "Human relatedness decays much slower than item
popularity."
So to make sure I understand the implications of using IDF… For
boolean/implicit preferences the sum of all prefs (after weighting) for a
single item over all users will always be 1 or 0. This no matter whether the
