Hi Manuel, Please send the paper as I don't have access. Thanks.
On Wed, Jan 4, 2012 at 11:02 PM, Manuel Blechschmidt <[email protected]> wrote: > Hello Nishant, > intent prediction based on the behavior on the website is a tough task. > > Here is a paper which trained bayes networks to guess the task that a person > is doing: > > An approach to situational market segmentation on on-line newspapers based on > current tasks > Anne Gutschmidt > http://dl.acm.org/citation.cfm?id=1864777 > > For the overall data set, we attained a prediction accuracy of 57.69%. > > If you do not have access to ACM portal. I can send you the paper manually. > > /Manuel > > On 04.01.2012, at 08:15, Nishant Chandra wrote: > >> 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 > > -- > Manuel Blechschmidt > Dortustr. 57 > 14467 Potsdam > Mobil: 0173/6322621 > Twitter: http://twitter.com/Manuel_B > -- Nishant Chandra Bangalore, India Cell : +91 9739131616
