That doesn't help the cold-start problem, of course. On Tue, Jan 3, 2012 at 8:07 PM, Lance Norskog <goks...@gmail.com> 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 <ted.dunn...@gmail.com> 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 <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 > >> > >> > >> > > > > -- > Lance Norskog > goks...@gmail.com >
