From user-return-11708-apmail-mahout-user-archive=mahout.apache.org@mahout.apache.org Tue Jan 3 21:45:17 2012 Return-Path: X-Original-To: apmail-mahout-user-archive@www.apache.org Delivered-To: apmail-mahout-user-archive@www.apache.org Received: from mail.apache.org (hermes.apache.org [140.211.11.3]) by minotaur.apache.org (Postfix) with SMTP id 521D39014 for ; Tue, 3 Jan 2012 21:45:17 +0000 (UTC) Received: (qmail 92242 invoked by uid 500); 3 Jan 2012 21:45:15 -0000 Delivered-To: apmail-mahout-user-archive@mahout.apache.org Received: (qmail 92207 invoked by uid 500); 3 Jan 2012 21:45:15 -0000 Mailing-List: contact user-help@mahout.apache.org; run by ezmlm Precedence: bulk List-Help: List-Unsubscribe: List-Post: List-Id: Reply-To: user@mahout.apache.org Delivered-To: mailing list user@mahout.apache.org Received: (qmail 92192 invoked by uid 99); 3 Jan 2012 21:45:15 -0000 Received: from athena.apache.org (HELO athena.apache.org) (140.211.11.136) by apache.org (qpsmtpd/0.29) with ESMTP; Tue, 03 Jan 2012 21:45:15 +0000 X-ASF-Spam-Status: No, hits=1.5 required=5.0 tests=HTML_MESSAGE,RCVD_IN_DNSWL_LOW,SPF_PASS X-Spam-Check-By: apache.org Received-SPF: pass (athena.apache.org: domain of ted.dunning@gmail.com designates 209.85.210.170 as permitted sender) Received: from [209.85.210.170] (HELO mail-iy0-f170.google.com) (209.85.210.170) by apache.org (qpsmtpd/0.29) with ESMTP; Tue, 03 Jan 2012 21:45:10 +0000 Received: by iafj26 with SMTP id j26so61902452iaf.1 for ; Tue, 03 Jan 2012 13:44:50 -0800 (PST) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=gmail.com; s=gamma; h=mime-version:in-reply-to:references:from:date:message-id:subject:to :content-type; bh=1mNpU1kzEyVN9DVc1Uu3xVubYR2FYKoKKDjQErD/Wag=; b=S0tjluVZRXniYnJOgZ/E0EBMLyPgMq8ZsSuBBJRgAEjz6uHOe42oJromc3WP4Oc65W kBiQUsiCPnTX4iAZsu5GybpUxaT+KPnFFwziROTbkTccPCJTZl2zJpRkDyQkFJOpCK1l DWIBDIbUvRuptWRwSpNKNsYNbjyGf8iiOUV6A= Received: by 10.42.150.130 with SMTP id a2mr55680720icw.43.1325627089112; Tue, 03 Jan 2012 13:44:49 -0800 (PST) MIME-Version: 1.0 Received: by 10.50.197.161 with HTTP; Tue, 3 Jan 2012 13:44:28 -0800 (PST) In-Reply-To: References: <74911649-7CCF-4766-BFDC-6EFD4262EAD2@gmx.de> From: Ted Dunning Date: Tue, 3 Jan 2012 13:44:28 -0800 Message-ID: Subject: Re: Purchase prediction To: user@mahout.apache.org Content-Type: multipart/alternative; boundary=90e6ba21220bc04d8204b5a69b3e --90e6ba21220bc04d8204b5a69b3e Content-Type: text/plain; charset=UTF-8 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 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 > 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 > > > --90e6ba21220bc04d8204b5a69b3e--