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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>BPR: Bayesian Personalized Ranking from Implicit Feedback · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1205.2618/</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/1205.2618/feed.xml" rel="self" type="application/rss+xml" /><item><title>BPR: Bayesian Personalized Ranking from Implicit Feedback — mentioned by andyxor</title><link>https://news.ycombinator.com/item?id=27868893</link><guid isPermaLink="false">urn:hackerpapers:mention:1205.2618:27868893</guid><pubDate>Sat, 17 Jul 2021 21:10:20 GMT</pubDate><description>&lt;p&gt;cool project, i was curious which algorithms are used for recommendations/ranking, couldn&amp;#x27;t find it in the docs, so had to go through the code, may be add a section on algorithms and models? it seems that one algo used is &amp;quot;Bayesian Personal Ranking&amp;quot; based on a paper from 2012 https://arxiv.org/pdf/1205.2618.pdf, here is a related blog post https://towardsdatascience.com/recommender-system-using-baye... another one is Weighted Regularized Matrix Factorization/ALS (by the Netflix Prize winner team): http://yifanhu.net/PUB/cf.pdf , also see…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1205.2618/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.IR</category></item></channel></rss>
