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 <[email protected]> To: [email protected] 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
