Hi Pat,

Yes, I understood  that, but it was sorts of curiosity to know what would
be the precise probability for a user to buy such recommended product. I do
understand, "The job of a recommender is not to predict what you *will* buy
but rather what you would like to buy"

Thank you for your prompt reply.

Thanks

On Wed, Apr 26, 2017 at 12:28 AM, Pat Ferrel <[email protected]> wrote:

> I read this dissertation and came away wondering why it was important. The
> job of a recommender is not to predict what you *will* buy but rather what
> you would like to buy if you knew about it—in other words it determines
> your taste or preferences and finds item that match. This tends to increase
> conversions (sales for E-Commerce). A predictor may only predict the
> inevitable and lead to 0 lift in conversions.
>
>
> On Apr 24, 2017, at 1:10 AM, Vaghawan Ojha <[email protected]> wrote:
>
> Hi,
>
> I was following a research paper regarding the probability of a user
> buying a particular item recommended by the recommendation system. It's
> here, if you want to checkout as well http://www.kecl.ntt.co.
> jp/as/members/iwata/doctor.pdf
>
> I was wondering if there is a way or anybody has done with the current
> templates of PIO, the calculation of the probability of a user buying an
> item.
>
> I think this should be possible with current templates as well, I am just
> wondering, if anybody could provide me a brief way to do that, or any
> documentation of the algorithms that could be used.
>
>
> Thanks
>
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