As for my use case and as Manuel pointed out is this: a. Given a set of page views happening in real time, will the visitor view another page on the site or will the visitor leave or is he comparing prices or just researching? The intention is what I want to capture. Building the model offline sounds like the right approach.
b. Given a set of page views, which product brand will the visitor view in the remainder of the session? This is an addon and I would like to explore it. To solve a), is HMM the right approach? Thanks, Nishant On Wed, Jan 4, 2012 at 10:15 AM, Ted Dunning <[email protected]> wrote: > That doesn't help the cold-start problem, of course. > > On Tue, Jan 3, 2012 at 8:07 PM, Lance Norskog <[email protected]> wrote: > >> If you can use an SVD-based recommender, here is a way to update an >> SVD in constant time that is much much smaller than the original >> decomposition. >> >> http://www.merl.com/papers/docs/TR2006-059.pdf >> >> On Tue, Jan 3, 2012 at 1:44 PM, Ted Dunning <[email protected]> wrote: >> > 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 <[email protected]> >> 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 <[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 >> >> >> >> >> >> >> >> >> >> -- >> Lance Norskog >> [email protected] >> -- Nishant Chandra Bangalore, India Cell : +91 9739131616
