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From Ted Dunning <ted.dunn...@gmail.com>
Subject Re: Purchase prediction
Date Tue, 03 Jan 2012 21:44:28 GMT
The recent data is usually just the user history, not the off-line
item-item relationship build.

For brand new items, there is the cold start problem, but this is often
handled by putting these items on a "New Arrivals" page so that you can
expose them to users until you get enough data to include them in the next
item-item build.  Enough data is usually around 10 clicks.

It is also plausible to cold-start items based on feature similarity.

On Tue, Jan 3, 2012 at 11:59 AM, Mike Spreitzer <mspreitz@us.ibm.com> wrote:

> I suspect the original request was concerned with --- and I, on my own, am
> concerned with --- a scenario in which it is desired to be able to quickly
> make predictions based on very recent data.  Thus, approaches that
> occasionally take a lot of time to build a model are non-solutions.  Are
> there solutions for my scenario in what you mentioned, or elsewhere?
>
> Thanks,
> Mike
>
>
>
> From:   Manuel Blechschmidt <Manuel.Blechschmidt@gmx.de>
> To:     user@mahout.apache.org
> Date:   01/03/2012 02:40 PM
> Subject:        Re: Purchase prediction
>
>
>
> Hello Nishan,
> you can use the recommender approaches with the boolean reference model.
>
> You can use IRStatistics (Precision, Recall, F-Measure) to benchmark your
> results.
>
> https://cwiki.apache.org/confluence/display/MAHOUT/Recommender+Documentation
>
>
> Further you could also use the hidden markov model to predict
> probabilities of next purchases.
> http://isabel-drost.de/hadoop/slides/HMM.pdf
> https://issues.apache.org/jira/browse/MAHOUT-396
>
> There are some papers describing how to combine some of these methods:
>
> Rendle. et. al presented a paper using a combination of both:
> Factorizing Personalized Markov Chains for Next-Basket Recommendation
>
> http://www.ismll.uni-hildesheim.de/pub/pdfs/RendleFreudenthaler2010-FPMC.pdf
>
>
> In my opinion some seasonal models could also help to better predict next
> purchases.
>
> There is currently an resolved enhancement request for 0.6 making
> evaluation for a use case like yours better:
>  https://issues.apache.org/jira/browse/MAHOUT-906
>
> If you have further questions feel free to ask.
>
> /Manuel
>
> On 03.01.2012, at 19:02, Nishant Chandra wrote:
>
> > Hi,
> >
> > I am trying to predict shopper purchase and non-purchase intention in
> > E-Commerce context. I am more interested in finding the later.
> > A near-real time approach will be great. So given a sequence of pages
> > a shopper views, I would like the algorithm to predict the intention.
> >
> > Any algorithms in Mahout or otherwise that can help?
> >
> > Thanks,
> > Nishant
>
> --
> Manuel Blechschmidt
> Dortustr. 57
> 14467 Potsdam
> Mobil: 0173/6322621
> Twitter: http://twitter.com/Manuel_B
>
>
>

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