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 <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 -- You received this message because you are subscribed to the Google Groups "actionml-user" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected] <mailto:[email protected]>. To post to this group, send email to [email protected] <mailto:[email protected]>. To view this discussion on the web visit https://groups.google.com/d/msgid/actionml-user/CA%2B69RXZnXGY_wMbU55fE_AW7Ur8YRyBkBJ%2BWcQXLKjRtPGi6tg%40mail.gmail.com <https://groups.google.com/d/msgid/actionml-user/CA%2B69RXZnXGY_wMbU55fE_AW7Ur8YRyBkBJ%2BWcQXLKjRtPGi6tg%40mail.gmail.com?utm_medium=email&utm_source=footer>. For more options, visit https://groups.google.com/d/optout <https://groups.google.com/d/optout>.
