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From Gökhan Çapan <>
Subject Re: Predicting Successor Item
Date Wed, 16 Jun 2010 07:05:10 GMT
I am not sure that I understood your suggestion correctly. But, I've come up
with an idea after reading.
If we create a dictionary-like structure with a high-weighted predecessor
field, and a previous items field whose entries are constructed like;
- an item as the key
- its predecessor item in predecessor field
- other previous items in the third field
Do you think results of a search query with user's recent history yields to
a reasonable, ranked list of  possible next items?

On Tue, Jun 15, 2010 at 8:12 PM, Ted Dunning <> wrote:

> You have most of the workings available to do a reasonable job of this in
> Mahout.  The simplest method in my mind is to grovel the logs and emit
> pairs
> of items with the key being the last item and previous items being the
> value.  Roughly this format should give you what you need for doing
> cooccurrence counting and LLR reduction.  The remaining pairs can be
> sparsified and indexed using Lucene and can probably also be fed into the
> Taste part of Mahout.  The default Lucene IDF weighting will do a decent
> job
> of emulating Naive Bayes so you can feed in a user's recent history as a
> query so that is a reasonable implementation as well.
> On Tue, Jun 15, 2010 at 3:38 AM, Gökhan Çapan <> 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
> >

Gökhan Çapan

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