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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Learning by Repetition: Stochastic Multi-armed Bandits under Priming Effect · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2006.10356/</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/2006.10356/feed.xml" rel="self" type="application/rss+xml" /><item><title>Learning by Repetition: Stochastic Multi-armed Bandits under Priming Effect — mentioned by theja</title><link>https://news.ycombinator.com/item?id=27112607</link><guid isPermaLink="false">urn:hackerpapers:mention:2006.10356:27112607</guid><pubDate>Tue, 11 May 2021 00:15:24 GMT</pubDate><description>&lt;p&gt;I had worked on an online machine learning method[0] last year that assumes priming effect (similar to the effect you and the GP describe, i.e., the timing effect) and tries to exploit it. With respect to infinite scrolling/ranking, I have also worked on a simplified model (unfortunately does not capture spacing of likes but captures fatigue based on relevancy) and and algorithm to work with it.[1] [0] https://arxiv.org/abs/2006.10356: Learning by Repetition: Stochastic Multi-armed Bandits under Priming Effect [1] https://arxiv.org/abs/1901.07734: Thompson Sampling for a Fatigue-aware Online…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2006.10356/"&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>
