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 <mspre...@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.blechschm...@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 > > >
