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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Cross-Device Tracking: Matching Devices and Cookies · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1510.01175/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/1510.01175/feed.xml" rel="self" type="application/rss+xml" /><item><title>Cross-Device Tracking: Matching Devices and Cookies — mentioned by hagy</title><link>https://news.ycombinator.com/item?id=27333930</link><guid isPermaLink="false">urn:hackerpapers:mention:1510.01175:27333930</guid><pubDate>Sun, 30 May 2021 15:30:45 GMT</pubDate><description>&lt;p&gt;Yep, this is called cross-device matching. Generally consists of some modeling for devices seen together on the same IP address. One of the notable AdTech companies in cross-device modeling is Drawbridge (purchased by LinkedIn). Here&amp;#x27;s a 2015 Kaggle competition that they hosted, which provides sample data that they use in modeling, https://www.kaggle.com/c/icdm-2015-drawbridge-cross-device-c... And here&amp;#x27;s a technical writeup of one of the well-performing solutions from that competition, https://arxiv.org/pdf/1510.01175.pdf&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1510.01175/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item></channel></rss>
