From user-return-17655-apmail-mahout-user-archive=mahout.apache.org@mahout.apache.org Tue Jun 18 18:17:13 2013 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 3AD2210D61 for ; Tue, 18 Jun 2013 18:17:13 +0000 (UTC) Received: (qmail 99367 invoked by uid 500); 18 Jun 2013 18:17:10 -0000 Delivered-To: apmail-mahout-user-archive@mahout.apache.org Received: (qmail 97610 invoked by uid 500); 18 Jun 2013 18:17:00 -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 97569 invoked by uid 99); 18 Jun 2013 18:16:58 -0000 Received: from athena.apache.org (HELO athena.apache.org) (140.211.11.136) by apache.org (qpsmtpd/0.29) with ESMTP; Tue, 18 Jun 2013 18:16:58 +0000 X-ASF-Spam-Status: No, hits=1.7 required=5.0 tests=FREEMAIL_ENVFROM_END_DIGIT,HTML_MESSAGE,RCVD_IN_DNSWL_LOW,SPF_PASS X-Spam-Check-By: apache.org Received-SPF: pass (athena.apache.org: domain of dlieu.7@gmail.com designates 209.85.219.42 as permitted sender) Received: from [209.85.219.42] (HELO mail-oa0-f42.google.com) (209.85.219.42) by apache.org (qpsmtpd/0.29) with ESMTP; Tue, 18 Jun 2013 18:16:54 +0000 Received: by mail-oa0-f42.google.com with SMTP id n12so5466301oag.1 for ; Tue, 18 Jun 2013 11:16:33 -0700 (PDT) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=gmail.com; s=20120113; h=mime-version:in-reply-to:references:date:message-id:subject:from:to :content-type; bh=0sWTCCSG8KDb3UcHw4lSOkovTJGQWDDTRgc6bZ/9nng=; b=dfX9M+B8WTgLM93wPwxFC1C+ff7beEJmksLloBwQggRvEAuIiDUFTlhyLkIeviQ6eW DnxLA0vKMer3akfjpHP3quvTDkWm+doRi7VYtj7R0K0oRUJ/Mi0nTuJ0gerzg77dc+Ix fjTH4WALEpD8Fg/Wh7S9eWdMNYr7vBGZ2cI22odSArAosuYkaQ3oMPTCYns/LzdVZX1V D5dZ2W9C3ojgmQDmM1ie2fo825NhEcC7WbdJY9Qk71fzZU2Z3/+7oVambCYoVJPfIbnu UvYMLyDMsJxMv47g4u9s8NQCTcazEPSnNrNixtOCl1gymUwzONjOyKUsjeMRsBQKreuv PjTQ== MIME-Version: 1.0 X-Received: by 10.60.52.109 with SMTP id s13mr12948158oeo.40.1371579393388; Tue, 18 Jun 2013 11:16:33 -0700 (PDT) Received: by 10.76.109.163 with HTTP; Tue, 18 Jun 2013 11:16:33 -0700 (PDT) In-Reply-To: <0E7F9B96-4E45-4028-A626-E7174BBB206E@occamsmachete.com> References: <0E7F9B96-4E45-4028-A626-E7174BBB206E@occamsmachete.com> Date: Tue, 18 Jun 2013 11:16:33 -0700 Message-ID: Subject: Re: Negative Preferences in a Recommender From: Dmitriy Lyubimov To: user@mahout.apache.org Content-Type: multipart/alternative; boundary=001a1132f8fa8638c004df71b633 X-Virus-Checked: Checked by ClamAV on apache.org --001a1132f8fa8638c004df71b633 Content-Type: text/plain; charset=ISO-8859-1 Koren, Volinsky: "CF for implicit feedback datasets" On Tue, Jun 18, 2013 at 8:07 AM, Pat Ferrel wrote: > They are on a lot of papers, which are you looking at? > > On Jun 17, 2013, at 6:30 PM, Dmitriy Lyubimov wrote: > > (Kinda doing something very close. ) > > Koren-Volynsky paper on implicit feedback can be generalized to decompose > all input into preference (0 or 1) and confidence matrices (which is > essentually an observation weight matrix). > > If you did not get any observations, you encode it as (p=0,c=1) but if you > know that user did not like item, you can encode that observation with much > more confidence weight, something like (p=0, c=30) -- actually as high > confidence as a conversion in your case it seems. > > The problem with this is that you end up with quite a bunch of additional > parameters in your model to figure, i.e. confidence weights for each type > of action in the system. You can establish that thru extensive > crossvalidation search, which is initially quite expensive (even for > distributed machine cluster tech), but could be incrementally bail out much > sooner after previous good guess is already known. > > MR doesn't work well for this though since it requires A LOT of > iterations. > > > > On Mon, Jun 17, 2013 at 5:51 PM, Pat Ferrel wrote: > > > In the case where you know a user did not like an item, how should the > > information be treated in a recommender? Normally for retail > > recommendations you have an implicit 1 for a purchase and no value > > otherwise. But what if you knew the user did not like an item? Maybe you > > have records of "I want my money back for this junk" reactions. > > > > You could make a scale, 0, 1 where 0 means a bad rating and 1 a good, no > > value as usual means no preference? Some of the math here won't work > though > > since usually no value implicitly = 0 so maybe -1 = bad, 1 = good, no > > preference implicitly = 0? > > > > Would it be better to treat the bad rating as a 1 and good as 2? This > > would be more like the old star rating method only we would know where > the > > cutoff should be between a good review and bad (1.5) > > > > I suppose this could also be treated as another recommender in an > ensemble > > where r = r_p - r_h, where r_h = predictions from "I hate this product" > > preferences? > > > > Has anyone found a good method? > > --001a1132f8fa8638c004df71b633--