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	<title>research &#8211; Lena S</title>
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		<title>MPhil Thesis on Click Modeling</title>
		<link>https://www.lenalenas.com/485/mphil-thesis-on-click-modeling/</link>
		
		<dc:creator><![CDATA[Lena]]></dc:creator>
		<pubDate>Mon, 23 Apr 2012 16:00:00 +0000</pubDate>
				<category><![CDATA[写写 | Blogue]]></category>
		<category><![CDATA[click model]]></category>
		<category><![CDATA[research]]></category>
		<category><![CDATA[search engine]]></category>
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					<description><![CDATA[This thesis was to fulfill my MPhil degree requirement. It contains a quick review of current click modeling techniques and provides new perspectives: (1) expanding query-document relevance score with a user dimension, hence personalized click models capturing user intrinsic preferences by matrix and tensor factorization; and (2) using previous click models as a micro layer for each user click out of a macro click chain, which includes search click logs for every click-able block on a whole search result page.]]></description>
		
		
		
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		<title>Personalized Click Model</title>
		<link>https://www.lenalenas.com/449/personalized-click-model/</link>
		
		<dc:creator><![CDATA[Lena]]></dc:creator>
		<pubDate>Wed, 08 Feb 2012 16:00:00 +0000</pubDate>
				<category><![CDATA[写写 | Blogue]]></category>
		<category><![CDATA[click model]]></category>
		<category><![CDATA[research]]></category>
		<category><![CDATA[search engine]]></category>
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					<description><![CDATA[We put forward a novel personalized click model to describe user-oriented click preferences, which applies and extends matrix / tensor factorization from the view of collaborative filtering to connect users, queries and documents together. Our model serves as a generalized personification framework that can be incorporated to the previously proposed click models and perhaps to their future expansions. A delightful bonus is the model's ability to get insights of queries and documents through latent feature vectors, and hence to handle rare and even new query-document pairs, that preceding click models could only take an average value.]]></description>
		
		
		
		<post-id xmlns="com-wordpress:feed-additions:1">449</post-id>	</item>
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		<title>Whole Page Click Model</title>
		<link>https://www.lenalenas.com/443/whole-page-click-model/</link>
		
		<dc:creator><![CDATA[Lena]]></dc:creator>
		<pubDate>Wed, 31 Aug 2011 15:15:46 +0000</pubDate>
				<category><![CDATA[写写 | Blogue]]></category>
		<category><![CDATA[click model]]></category>
		<category><![CDATA[research]]></category>
		<category><![CDATA[search engine]]></category>
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					<description><![CDATA[When searching for information on a search result page, one is often interacting with an entire page instead of a single block (ads block or organic search block). In this paper, we put forward a novel Whole Page Click (WPC) Model to characterize user behavior in multiple blocks. Specifically, WPC uses a Markov chain to learn the user transition probabilities among different blocks in the whole page. WPC can achieve significant gain in interpreting the advertisement data.]]></description>
		
		
		
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