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	<title>Comments on: Maximum Margin Matrix Factorization</title>
	<atom:link href="http://blog.tech.stylefeeder.com/2007/07/30/maximum-margin-matrix-factorization/feed/" rel="self" type="application/rss+xml" />
	<link>http://blog.tech.stylefeeder.com/2007/07/30/maximum-margin-matrix-factorization/</link>
	<description>Bitheads Invade the Fashion World</description>
	<pubDate>Fri, 29 Aug 2008 03:00:28 +0000</pubDate>
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		<item>
		<title>By: Jason Rennie</title>
		<link>http://blog.tech.stylefeeder.com/2007/07/30/maximum-margin-matrix-factorization/#comment-63</link>
		<dc:creator>Jason Rennie</dc:creator>
		<pubDate>Tue, 27 May 2008 19:09:26 +0000</pubDate>
		<guid isPermaLink="false">http://blog.tech.stylefeeder.com/2007/07/30/maximum-margin-matrix-factorization/#comment-63</guid>
		<description>Hi Manu,

Yes, MMMF certainly can be used for binary data.  MMMF can be applied to data with any number of ordinal labels &gt;= 2.  In both the original NIPS MMMF paper, as well as the ICML Fast MMMF paper, you may have noticed that we used `R' to represent the number of ordinal labels.  You can simply set R=2 for binary data.  This should simplify many of the expressions.  For my MMMF matlab code, pass 2 for the value of l for the gradient/objective function.

Jason</description>
		<content:encoded><![CDATA[<p>Hi Manu,</p>
<p>Yes, MMMF certainly can be used for binary data.  MMMF can be applied to data with any number of ordinal labels >= 2.  In both the original NIPS MMMF paper, as well as the ICML Fast MMMF paper, you may have noticed that we used `R&#8217; to represent the number of ordinal labels.  You can simply set R=2 for binary data.  This should simplify many of the expressions.  For my MMMF matlab code, pass 2 for the value of l for the gradient/objective function.</p>
<p>Jason</p>
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	<item>
		<title>By: manu</title>
		<link>http://blog.tech.stylefeeder.com/2007/07/30/maximum-margin-matrix-factorization/#comment-59</link>
		<dc:creator>manu</dc:creator>
		<pubDate>Wed, 16 Apr 2008 15:10:04 +0000</pubDate>
		<guid isPermaLink="false">http://blog.tech.stylefeeder.com/2007/07/30/maximum-margin-matrix-factorization/#comment-59</guid>
		<description>Hi,
Can MMMF be used for recommendation based on binary data (ie: i like/dislike this item) instead of rating data ? If so, could you point out any relevant publication.
I have trouble to figure that out.
Thank you.</description>
		<content:encoded><![CDATA[<p>Hi,<br />
Can MMMF be used for recommendation based on binary data (ie: i like/dislike this item) instead of rating data ? If so, could you point out any relevant publication.<br />
I have trouble to figure that out.<br />
Thank you.</p>
]]></content:encoded>
	</item>
	<item>
		<title>By: Jason Rennie</title>
		<link>http://blog.tech.stylefeeder.com/2007/07/30/maximum-margin-matrix-factorization/#comment-55</link>
		<dc:creator>Jason Rennie</dc:creator>
		<pubDate>Mon, 18 Feb 2008 21:01:14 +0000</pubDate>
		<guid isPermaLink="false">http://blog.tech.stylefeeder.com/2007/07/30/maximum-margin-matrix-factorization/#comment-55</guid>
		<description>Hi Ruth,

MMMF can certainly be used for search.  Here at StyleFeeder, we use it to provide personalized search results, based on the ratings and bookmarks the user has provided.

By "vector space model", I'm guessing that you mean a dimensionality-reduction of the term-document matrix.  We don't use MMMF for that, but you certainly could.  You'd need a different loss function (e.g. one based on the multinomial model).  With that and the trace norm regularization term, you'd get a convex objective which yields a low-rank solution given a sufficiently large weight on the regularization term.  Do contact me if you have further interest/questions.

Jason</description>
		<content:encoded><![CDATA[<p>Hi Ruth,</p>
<p>MMMF can certainly be used for search.  Here at StyleFeeder, we use it to provide personalized search results, based on the ratings and bookmarks the user has provided.</p>
<p>By &#8220;vector space model&#8221;, I&#8217;m guessing that you mean a dimensionality-reduction of the term-document matrix.  We don&#8217;t use MMMF for that, but you certainly could.  You&#8217;d need a different loss function (e.g. one based on the multinomial model).  With that and the trace norm regularization term, you&#8217;d get a convex objective which yields a low-rank solution given a sufficiently large weight on the regularization term.  Do contact me if you have further interest/questions.</p>
<p>Jason</p>
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	<item>
		<title>By: ruth</title>
		<link>http://blog.tech.stylefeeder.com/2007/07/30/maximum-margin-matrix-factorization/#comment-54</link>
		<dc:creator>ruth</dc:creator>
		<pubDate>Sun, 10 Feb 2008 10:54:03 +0000</pubDate>
		<guid isPermaLink="false">http://blog.tech.stylefeeder.com/2007/07/30/maximum-margin-matrix-factorization/#comment-54</guid>
		<description>Hi,

Can MMMF be used for vector space model search engine, such as SVD or NNMF (non-negative matrix factorisation) ?  IF so, could you please point out any publication that MMMF is used for search engine?  I have Google, but to no luck at all.</description>
		<content:encoded><![CDATA[<p>Hi,</p>
<p>Can MMMF be used for vector space model search engine, such as SVD or NNMF (non-negative matrix factorisation) ?  IF so, could you please point out any publication that MMMF is used for search engine?  I have Google, but to no luck at all.</p>
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		<title>By: armynavy</title>
		<link>http://blog.tech.stylefeeder.com/2007/07/30/maximum-margin-matrix-factorization/#comment-43</link>
		<dc:creator>armynavy</dc:creator>
		<pubDate>Thu, 22 Nov 2007 03:23:22 +0000</pubDate>
		<guid isPermaLink="false">http://blog.tech.stylefeeder.com/2007/07/30/maximum-margin-matrix-factorization/#comment-43</guid>
		<description>My Style twins don't appear at all similar in tastes?
Why aren't these updated with our own recommendations and knowledge?</description>
		<content:encoded><![CDATA[<p>My Style twins don&#8217;t appear at all similar in tastes?<br />
Why aren&#8217;t these updated with our own recommendations and knowledge?</p>
]]></content:encoded>
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		<title>By: Whirlycott / Philip Jacob &#187; StyleFeeder Tech Blog</title>
		<link>http://blog.tech.stylefeeder.com/2007/07/30/maximum-margin-matrix-factorization/#comment-2</link>
		<dc:creator>Whirlycott / Philip Jacob &#187; StyleFeeder Tech Blog</dc:creator>
		<pubDate>Mon, 30 Jul 2007 20:55:20 +0000</pubDate>
		<guid isPermaLink="false">http://blog.tech.stylefeeder.com/2007/07/30/maximum-margin-matrix-factorization/#comment-2</guid>
		<description>[...] Blog for those of you who are interested in the black magic that goes on behind the scenes here.  Jason&#8217;s been doing all of the writing so far, mainly about our CF recommendation engine and Fast [...]</description>
		<content:encoded><![CDATA[<p>[...] Blog for those of you who are interested in the black magic that goes on behind the scenes here.  Jason&#8217;s been doing all of the writing so far, mainly about our CF recommendation engine and Fast [...]</p>
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