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
