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From Ted Dunning <ted.dunn...@gmail.com>
Subject Re: Purchase prediction
Date Wed, 04 Jan 2012 17:11:42 GMT
Decision tree learning is fine for relatively small data, but it doesn't
model latent variables directly.  You can use any supervised classifier as
a component of something like a conditional random field, but the use of
decision tree learning isn't a deciding factor.

HMM's are a form of sequential pattern mining.  Most forms, however, don't
handle latent factors well since this method usually tries to predict based
only on recent events.

On Wed, Jan 4, 2012 at 8:44 AM, Nishant Chandra
<nishant.chandra@gmail.com>wrote:

> How about using decision tree learning or sequential pattern mining?
> Any thoughts?
>
> Thanks,
> Nishant
>
> On Wed, Jan 4, 2012 at 1:25 PM, Ted Dunning <ted.dunning@gmail.com> wrote:
> > On Tue, Jan 3, 2012 at 11:15 PM, Nishant Chandra
> > <nishant.chandra@gmail.com>wrote:
> >
> >> As for my use case and as Manuel pointed out is this:
> >>
> >> a. Given a set of page views happening in real time, will the visitor
> >> view another page on the site or will the visitor leave or is he
> >> comparing prices or just researching? The intention is what I want to
> >> capture. Building the model offline sounds like the right approach.
> >>
> > ...
> >
> > To solve a), is HMM the right approach?
> >
> >
> > It is a plausible approach.  But not the only one.  It is attractive in
> > that it tries to model intent.
> >
> > You might also look at something like a latent log-linear model.  That
> > would allow you to model per user bias in intent.
> >
> >
> >> b. Given a set of page views, which product brand will the visitor
> >> view in the remainder of the session? This is an addon and I would
> >> like to explore it.
> >>
> >
> > This is a reasonable task for recommendation engines.
>

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