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]
