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From Manuel Blechschmidt <Manuel.Blechschm...@gmx.de>
Subject Re: Purchase prediction
Date Tue, 03 Jan 2012 20:26:31 GMT
Hi Mike,
actually it is a very tough research task to make predictions in real time.

I would expect that you can tune hidden markov models to work in semi real time.

Further if you have a trained model you can use this model in real time. The big question
is how often can and should you rebuild your model. Further the question is how much computation
time do you want to spend for every customer?

Perhaps the KDD Cup from 2000 is valueable:
http://www.kdd.org/kddcup/index.php?section=2000&method=result

Tasks:
Given a set of page views, will the visitor view another page on the site or will the visitor
leave?
Given a set of page views, which product brand will the visitor view in the remainder of the
session?
...

Agrawal et al. described a method to semi real time recommendations for news stories:
Fast Online Learning through Offline Initialization for Time-sensitive Recommendation
http://users.cs.fiu.edu/~lzhen001/activities/KDD_USB_key_2010/docs/p703.pdf

Hope that helps. If you have any results I would be interested in them.

/Manuel


On 03.01.2012, at 20:59, Mike Spreitzer 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
> 
> 

-- 
Manuel Blechschmidt
Dortustr. 57
14467 Potsdam
Mobil: 0173/6322621
Twitter: http://twitter.com/Manuel_B


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