From user-return-11707-apmail-mahout-user-archive=mahout.apache.org@mahout.apache.org Tue Jan 3 21:33:45 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 996A69B74 for ; Tue, 3 Jan 2012 21:33:45 +0000 (UTC) Received: (qmail 63702 invoked by uid 500); 3 Jan 2012 21:33:44 -0000 Delivered-To: apmail-mahout-user-archive@mahout.apache.org Received: (qmail 63672 invoked by uid 500); 3 Jan 2012 21:33:44 -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 63664 invoked by uid 99); 3 Jan 2012 21:33:44 -0000 Received: from nike.apache.org (HELO nike.apache.org) (192.87.106.230) by apache.org (qpsmtpd/0.29) with ESMTP; Tue, 03 Jan 2012 21:33:44 +0000 X-ASF-Spam-Status: No, hits=-0.7 required=5.0 tests=RCVD_IN_DNSWL_LOW,SPF_PASS X-Spam-Check-By: apache.org Received-SPF: pass (nike.apache.org: domain of ssc.open@googlemail.com designates 209.85.215.170 as permitted sender) Received: from [209.85.215.170] (HELO mail-ey0-f170.google.com) (209.85.215.170) by apache.org (qpsmtpd/0.29) with ESMTP; Tue, 03 Jan 2012 21:33:37 +0000 Received: by eaa13 with SMTP id 13so14871417eaa.1 for ; Tue, 03 Jan 2012 13:33:17 -0800 (PST) Received: by 10.204.145.69 with SMTP id c5mr12583625bkv.80.1325626396297; Tue, 03 Jan 2012 13:33:16 -0800 (PST) Received: from [192.168.0.103] (g231192155.adsl.alicedsl.de. [92.231.192.155]) by mx.google.com with ESMTPS id a17sm101835236bkz.7.2012.01.03.13.33.13 (version=SSLv3 cipher=OTHER); Tue, 03 Jan 2012 13:33:14 -0800 (PST) Message-ID: <4F037418.4020203@apache.org> Date: Tue, 03 Jan 2012 22:33:12 +0100 From: Sebastian Schelter Reply-To: ssc@apache.org User-Agent: Mozilla/5.0 (X11; U; Linux x86_64; en-US; rv:1.9.2.24) Gecko/20111108 Lightning/1.0b2 Thunderbird/3.1.16 MIME-Version: 1.0 To: user@mahout.apache.org Subject: Re: Purchase prediction References: <74911649-7CCF-4766-BFDC-6EFD4262EAD2@gmx.de> In-Reply-To: X-Enigmail-Version: 1.1.2 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: 7bit X-Virus-Checked: Checked by ClamAV on apache.org A very simple approach would be to use an item-based recommender with a precomputed model (that might be a day old) and simply use the items most similar to the latest items the user preferred as recommendations. These recommendations can be found in "real time" where "real time" means that a user fills a shopping cart and his recommendations are immediately updated after each item he adds. --sebastian On 03.01.2012 20:59, 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 >