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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Binary Latent Representations for Efficient Ranking: Empirical Assessment · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1706.07479/</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/1706.07479/feed.xml" rel="self" type="application/rss+xml" /><item><title>Binary Latent Representations for Efficient Ranking: Empirical Assessment — mentioned by Radim</title><link>https://news.ycombinator.com/item?id=16627200</link><guid isPermaLink="false">urn:hackerpapers:mention:1706.07479:16627200</guid><pubDate>Tue, 20 Mar 2018 10:09:15 GMT</pubDate><description>&lt;p&gt;Optimizations are always great :-) This sounds similar to Maciej Kula&amp;#x27;s experiments in &amp;quot;Binary Latent Representations for Efficient Ranking: Empirical Assessment&amp;quot;, https://arxiv.org/abs/1706.07479. Maciej shared this [1]: &amp;quot;FWIW I ran similar experiments on recommendation tasks. My initial results were very encouraging (and similar to those reported here), but the effect disappeared entirely after adding regularization to the baseline. I would have more confidence in the results reported here if the authors ran a more principled hyperparameter search, and added regularization to their…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1706.07479/"&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>
