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	<title>Comments on: SIGIR 2010: Day 1 Technical Sessions</title>
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		<title>By: Daniel Tunkelang</title>
		<link>http://thenoisychannel.com/2010/07/21/sigir-2010-day-1-technical-sessions/comment-page-1/#comment-6523</link>
		<dc:creator>Daniel Tunkelang</dc:creator>
		<pubDate>Sun, 25 Jul 2010 13:25:02 +0000</pubDate>
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		<description>Dinesh, content-based recommendations of movies based on similarity to a single movie are probably intuitive enough for users not to require a reductionist explanation of the similarity factors. That&#039;s much less true for recommendations based on a set or sequence of movies, especially when the recommendations aren&#039;t just content-based (e.g., collaborative filtering).

Andy, you can read the full paper &lt;a href=&quot;http://portal.acm.org/citation.cfm?id=1835449.1835484&quot; rel=&quot;nofollow&quot;&gt;here&lt;/a&gt; if you have access to the ACM Digital Library. I wish authors would be more be more consistent about posting their papers on their own web sites!</description>
		<content:encoded><![CDATA[<p>Dinesh, content-based recommendations of movies based on similarity to a single movie are probably intuitive enough for users not to require a reductionist explanation of the similarity factors. That&#8217;s much less true for recommendations based on a set or sequence of movies, especially when the recommendations aren&#8217;t just content-based (e.g., collaborative filtering).</p>
<p>Andy, you can read the full paper <a href="http://portal.acm.org/citation.cfm?id=1835449.1835484" rel="nofollow">here</a> if you have access to the ACM Digital Library. I wish authors would be more be more consistent about posting their papers on their own web sites!</p>
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		<title>By: Andy</title>
		<link>http://thenoisychannel.com/2010/07/21/sigir-2010-day-1-technical-sessions/comment-page-1/#comment-6511</link>
		<dc:creator>Andy</dc:creator>
		<pubDate>Thu, 22 Jul 2010 13:58:57 +0000</pubDate>
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		<description>How did the IBM researchers combine and rank the results from the two recommender models (tag-based and people-based)?</description>
		<content:encoded><![CDATA[<p>How did the IBM researchers combine and rank the results from the two recommender models (tag-based and people-based)?</p>
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		<title>By: Dinesh Vadhia</title>
		<link>http://thenoisychannel.com/2010/07/21/sigir-2010-day-1-technical-sessions/comment-page-1/#comment-6509</link>
		<dc:creator>Dinesh Vadhia</dc:creator>
		<pubDate>Thu, 22 Jul 2010 09:10:53 +0000</pubDate>
		<guid isPermaLink="false">http://thenoisychannel.com/?p=3204#comment-6509</guid>
		<description>@ daniel, re: Recommendation engines cry out for transparency

Re-read the original post on this and still not sure that I understand where you&#039;re coming from.  For example, if the last movie I watched (with the kids!) was &quot;lilo and stitch&quot; and we enter it into a movie recommendation engine I&#039;d expect to find similar movies to that one.  

Are you saying that this is not what today&#039;s recommendation engines do?</description>
		<content:encoded><![CDATA[<p>@ daniel, re: Recommendation engines cry out for transparency</p>
<p>Re-read the original post on this and still not sure that I understand where you&#8217;re coming from.  For example, if the last movie I watched (with the kids!) was &#8220;lilo and stitch&#8221; and we enter it into a movie recommendation engine I&#8217;d expect to find similar movies to that one.  </p>
<p>Are you saying that this is not what today&#8217;s recommendation engines do?</p>
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