ver fitting, while
big lambda like 300 removes over fitting but the nb of diff items on the top
1 and top 5 of the preference list is very small (not personalized).
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On Thu, Jun 19, 2014 at 3:44 PM, redocpot wrote:
> As the paper said, the low ratings will get a low confidence weight, so if I
> understand correctly, these dominant one-timers will be more *unlikely* to
> be recommended comparing to other items whose nbPurchase is bigger.
Correct, yes.
> In f
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On Thu, Jun 19, 2014 at 3:03 PM, redocpot wrote:
> We did some sanity check. For example, each user has his own item list which
> is sorted by preference, then we just pick the top 10 items for each user.
> As a result, we found that there were only 169 different items among the
> (1060080 x 10) i
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On Thu, Jun 5, 2014 at 10:38 PM, redocpot wrote:
> can be simplified by taking advantage of its algebraic structure, so
> negative observations are not needed. This is what I think at the first time
> I read the paper.
Correct, a big part of the reason that is efficient is because of
sparsity of
ad the ALS implementation for more
details.
Hao
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it
> has already done that ? In that case, I could simply pass only the positive
> observation as the explicit ALS does.
>
> Hao.
>
>
>
>
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plicit ALS does.
Hao.
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