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	<id>https://nmsl.cs.sfu.ca/index.php?action=history&amp;feed=atom&amp;title=Advertising_in_Online_Videos</id>
	<title>Advertising in Online Videos - Revision history</title>
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	<updated>2026-11-14T02:53:57Z</updated>
	<subtitle>Revision history for this page on the wiki</subtitle>
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	<entry>
		<id>https://nmsl.cs.sfu.ca/index.php?title=Advertising_in_Online_Videos&amp;diff=3954&amp;oldid=prev</id>
		<title>Hsadeghi at 20:41, 10 January 2011</title>
		<link rel="alternate" type="text/html" href="https://nmsl.cs.sfu.ca/index.php?title=Advertising_in_Online_Videos&amp;diff=3954&amp;oldid=prev"/>
		<updated>2011-01-10T20:41:21Z</updated>

		<summary type="html">&lt;p&gt;&lt;/p&gt;
&lt;table class=&quot;diff diff-contentalign-left diff-editfont-monospace&quot; data-mw=&quot;interface&quot;&gt;
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				&lt;td colspan=&quot;2&quot; style=&quot;background-color: #fff; color: #202122; text-align: center;&quot;&gt;← Older revision&lt;/td&gt;
				&lt;td colspan=&quot;2&quot; style=&quot;background-color: #fff; color: #202122; text-align: center;&quot;&gt;Revision as of 20:41, 10 January 2011&lt;/td&gt;
				&lt;/tr&gt;&lt;tr&gt;&lt;td colspan=&quot;2&quot; class=&quot;diff-lineno&quot; id=&quot;mw-diff-left-l32&quot; &gt;Line 32:&lt;/td&gt;
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&lt;tr&gt;&lt;td class='diff-marker'&gt; &lt;/td&gt;&lt;td style=&quot;background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;== On-going Research Problems ==  &lt;/div&gt;&lt;/td&gt;&lt;td class='diff-marker'&gt; &lt;/td&gt;&lt;td style=&quot;background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;== On-going Research Problems ==  &lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td class='diff-marker'&gt; &lt;/td&gt;&lt;td style=&quot;background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;/td&gt;&lt;td class='diff-marker'&gt; &lt;/td&gt;&lt;td style=&quot;background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td class='diff-marker'&gt;−&lt;/td&gt;&lt;td style=&quot;color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;&lt;del class=&quot;diffchange diffchange-inline&quot;&gt;Estimating the click-through rate &lt;/del&gt;for &lt;del class=&quot;diffchange diffchange-inline&quot;&gt;new ads with semantic and feature based similarity algorithms&lt;/del&gt;&lt;/div&gt;&lt;/td&gt;&lt;td class='diff-marker'&gt;+&lt;/td&gt;&lt;td style=&quot;color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;&lt;ins class=&quot;diffchange diffchange-inline&quot;&gt;SmartAd: A Smart System &lt;/ins&gt;for &lt;ins class=&quot;diffchange diffchange-inline&quot;&gt;Effective Advertising in Online Videos&lt;/ins&gt;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td class='diff-marker'&gt; &lt;/td&gt;&lt;td style=&quot;background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;/td&gt;&lt;td class='diff-marker'&gt; &lt;/td&gt;&lt;td style=&quot;background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;/td&gt;&lt;/tr&gt;
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&lt;/table&gt;</summary>
		<author><name>Hsadeghi</name></author>
	</entry>
	<entry>
		<id>https://nmsl.cs.sfu.ca/index.php?title=Advertising_in_Online_Videos&amp;diff=3953&amp;oldid=prev</id>
		<title>Hsadeghi: New page: Advertising in online videos is a large and growing market. In this paper, we propose a new approach to match ads with online videos based on the shopping interests of the target audience ...</title>
		<link rel="alternate" type="text/html" href="https://nmsl.cs.sfu.ca/index.php?title=Advertising_in_Online_Videos&amp;diff=3953&amp;oldid=prev"/>
		<updated>2011-01-10T20:40:15Z</updated>

		<summary type="html">&lt;p&gt;New page: Advertising in online videos is a large and growing market. In this paper, we propose a new approach to match ads with online videos based on the shopping interests of the target audience ...&lt;/p&gt;
&lt;p&gt;&lt;b&gt;New page&lt;/b&gt;&lt;/p&gt;&lt;div&gt;Advertising in online videos is a large and&lt;br /&gt;
growing market. In this paper, we propose a new approach&lt;br /&gt;
to match ads with online videos based on the shopping&lt;br /&gt;
interests of the target audience of videos. The proposed&lt;br /&gt;
approach increases the relevance of ads to the actual&lt;br /&gt;
viewers (humans) of videos, which increases the number&lt;br /&gt;
of users who purchase goods and services offered by&lt;br /&gt;
the advertisers. This in turn will increase the revenues&lt;br /&gt;
for advertisers as well as the video sites as video sites&lt;br /&gt;
usually charge advertisers based on the number of user&lt;br /&gt;
clicks on their ads. The proposed approach is different&lt;br /&gt;
from current approaches used in practice or proposed in&lt;br /&gt;
the literatures, which most of them try to maximize the&lt;br /&gt;
relevance of ads to the tags or contents of videos (objects).&lt;br /&gt;
We conduct a subjective study to evaluate the performance&lt;br /&gt;
of the proposed approach on many videos retrieved from&lt;br /&gt;
YouTube. Our results show that the proposed approach&lt;br /&gt;
yields more relevant ads to the viewers than the YouTube’s&lt;br /&gt;
approach. We also compare against other approaches&lt;br /&gt;
proposed in the literature and we show that the new&lt;br /&gt;
approach outperforms them.&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== People ==&lt;br /&gt;
&lt;br /&gt;
* [http://www.cs.sfu.ca/~mhefeeda/ Mohamed Hefeeda]&lt;br /&gt;
&lt;br /&gt;
* [http://www.cs.sfu.ca/~hsadeghi/ Hamed Sadeghi Neshat (MSc student)]&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== On-going Research Problems == &lt;br /&gt;
&lt;br /&gt;
Estimating the click-through rate for new ads with semantic and feature based similarity algorithms&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== References and Links  ==&lt;/div&gt;</summary>
		<author><name>Hsadeghi</name></author>
	</entry>
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