There is a subtle but very important point here.

This does not give you the probability of a user buying a recommendation it 
gives you the probability that a user will buy a product. You will see that a 
user buys a very small % products at Amazon, so if the technique is extremely 
accurate it will tell you the probability is 0 for the vast majority of 
products. 

A recommender works quite differently, getting a user to convert on things that 
can’t be predicted from only their history. This is the "collaborative 
filtering” part. The recommender looks at the behavior of other people to 
recommend, therefor it is not trying to answer the question of what an 
individual is going to do but what they might do if given the choice. In this 
sense it is part of your discovery tools including search and browsing.

You might combine the 2 by getting recommendations, then asking this other 
technique for the probability a user will buy any of the recommendations but 
what would you do with the information? They may seem like that same thing but 
one will lead to conversion lift and (if the other operates perfectly) the 
other will not.


On Apr 26, 2017, at 5:04 AM, Vaghawan Ojha <vaghawan...@gmail.com> wrote:

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 <p...@occamsmachete.com 
<mailto:p...@occamsmachete.com>> 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 <vaghawan...@gmail.com 
<mailto:vaghawan...@gmail.com>> 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 
<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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