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	<title>matrix factorization Archives - Petamind</title>
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	<title>matrix factorization Archives - Petamind</title>
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		<title>MLP for implicit binary collaborative filtering</title>
		<link>https://petaminds.com/mlp-for-implicit-binary-collaborative-filtering/</link>
					<comments>https://petaminds.com/mlp-for-implicit-binary-collaborative-filtering/#respond</comments>
		
		<dc:creator><![CDATA[Tung Nguyen]]></dc:creator>
		<pubDate>Mon, 02 Mar 2020 12:11:05 +0000</pubDate>
				<category><![CDATA[data science]]></category>
		<category><![CDATA[Project]]></category>
		<category><![CDATA[Research]]></category>
		<category><![CDATA[collaborative filtering]]></category>
		<category><![CDATA[keras]]></category>
		<category><![CDATA[matrix factorization]]></category>
		<category><![CDATA[MLP]]></category>
		<category><![CDATA[multi-layer perceptron]]></category>
		<guid isPermaLink="false">https://petaminds.com/?p=2265</guid>

					<description><![CDATA[<p>In this post, we demonstrate Keras implementation of the implicit collaborative filtering. We also introduce some techniques to improve the performance of the current model, including weight initialization, dynamic learning rate, early stopping callback etc. The implicit data For demonstration purposes, we use the dataset generated from negative samples using the technique mentioned in this [&#8230;]</p>
<p>The post <a href="https://petaminds.com/mlp-for-implicit-binary-collaborative-filtering/">MLP for implicit binary collaborative filtering</a> appeared first on <a href="https://petaminds.com">Petamind</a>.</p>
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