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From Sean Owen <sro...@gmail.com>
Subject Re: Predicting Successor Item
Date Tue, 15 Jun 2010 10:58:22 GMT
I would strongly guess that it's the very last item purchased that
makes the most difference to the next item purchased. So it's probably
a fairly simple problem -- just look at chains of length 1. Count for
each item i, which item j came next. Then just return the
highest-count one.

You could throw HMM at it but I suspect it's overkill.

Frequent item set mining is also probably applicable here. Given 2
items in a cart, which 3rd item is most probable?

On Tue, Jun 15, 2010 at 11:38 AM, Gökhan Çapan <gkhncpn@gmail.com> wrote:
> Hi,
> This is not a question specific to Mahout library. I hope you'll be
> interested.
>
> While recommending  to a user, we take his ratings to items, or some
> implicit ratings like his purchase history, click history, etc. into
> account. Item based collaborative filtering techniques generally compute
> item-to-item similarities in a symmetrical way ( sim(item1,item2) =
> sim(item2,item1). This is the nature of a distance measure).
>
> What if we consider user's historical data as a sequence, and want to
> predict the successor item? For example, in an e-commerce domain, we may
> want to find the item to buy after buying some other items. For example, if
> we have a user vector u, where uti is the item that user was interested in
> time ti, what are the possible values of ucurrent?
>
> Considering active user's interest to items at a specific time as states,
> can we see predicting user's current interest as the unobserved state and
> the user data as an HMM? I do not know well HMM, do you think that point of
> view to the problem seems reasonable? Do you have any ideas/suggestions
> about other solutions if it is not a good way?
> --
> Gökhan Çapan
>

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