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	<title>apsquared.net &#187; Taste</title>
	<link>http://www.apsquared.net/blog</link>
	<description>Web Development and Technology Blog.</description>
	<pubDate>Thu, 24 Jul 2008 23:58:09 +0000</pubDate>
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		<title>Recommendation Engine Achieves 85% Success</title>
		<link>http://www.apsquared.net/blog/2008/04/24/recommendation-engine-achieves-85-success/</link>
		<comments>http://www.apsquared.net/blog/2008/04/24/recommendation-engine-achieves-85-success/#comments</comments>
		<pubDate>Thu, 24 Apr 2008 00:10:00 +0000</pubDate>
		<dc:creator>fmapap</dc:creator>
		
		<category><![CDATA[Collaborative Filtering]]></category>

		<category><![CDATA[MyFriendSuggests]]></category>

		<category><![CDATA[Taste]]></category>

		<category><![CDATA[MyFriendSuggests.com]]></category>

		<category><![CDATA[Recommendation Engine]]></category>

		<category><![CDATA[Suggestions]]></category>
<category>collaborative filtering</category><category>MyFriendSuggests.com</category><category>Recommendation Engine</category><category>suggestions</category>
		<guid isPermaLink="false">http://www.apsquared.net/blog/2008/04/24/recommendation-engine-achieves-85-success/</guid>
		<description><![CDATA[As we&#8217;ve been able to gather more rating data from our site, MyFriendSuggests.com we&#8217;ve been able to run more tests on our unique recommendation algorithm.   Our algorithm uniques combines both user based and item based collaborative filtering.  Our recent tweaks to the algorithm have shown improvements and we are now able to [...]]]></description>
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		<title>Creating a custom recommender using taste</title>
		<link>http://www.apsquared.net/blog/2007/05/29/creating-a-custom-recommender-using-taste/</link>
		<comments>http://www.apsquared.net/blog/2007/05/29/creating-a-custom-recommender-using-taste/#comments</comments>
		<pubDate>Tue, 29 May 2007 19:19:59 +0000</pubDate>
		<dc:creator>fmapap</dc:creator>
		
		<category><![CDATA[Java]]></category>

		<category><![CDATA[MyFriendSuggests]]></category>

		<category><![CDATA[Taste]]></category>
<category>collaborative filtering</category><category>java</category><category>Programming</category><category>recommender</category><category>taste</category><category>Web 2.0</category>
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		<description><![CDATA[Taste is a great framework for collaborative filtering.  We are going to be launching a new recommendation algorithm on our site (MyFriendSuggests.com) in the coming weeks (Stay Tuned!) based on the Taste framework.  Taste provides a User-based and Item-based recommender.  User based recommenders find users that have similiar tastes to you and then use their [...]]]></description>
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		<title>Improving performance of Taste using DBCP</title>
		<link>http://www.apsquared.net/blog/2007/04/25/improving-performance-of-taste-using-dbcp/</link>
		<comments>http://www.apsquared.net/blog/2007/04/25/improving-performance-of-taste-using-dbcp/#comments</comments>
		<pubDate>Wed, 25 Apr 2007 03:18:36 +0000</pubDate>
		<dc:creator>fmapap</dc:creator>
		
		<category><![CDATA[Java]]></category>

		<category><![CDATA[MyFriendSuggests]]></category>

		<category><![CDATA[Taste]]></category>

		<category><![CDATA[Web Development]]></category>
<category>java</category><category>MyFriendSuggests</category><category>taste</category><category>Technorati</category><category>Web 2.0</category>
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		<description><![CDATA[For the past few weeks I&#8217;ve been playing with Taste, a Java based framework for collaborative filtering (basically the recommendation feature found on sites like Amazon and Netflix).    Hopefully in the near feature this tool will be incorporated in our site, MyFriendSuggests.com to improve our suggestion algorithms. 
What I found was the initial description of using [...]]]></description>
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