<story><title>Categories of deep recommendation systems</title><url>https://jameskle.com/writes/rec-sys-part-2</url></story><parent_chain></parent_chain><comment><author>fjabre</author><text>The recommendation systems (i.e. Netflix) for movies and music are still just awful in terms of guessing my tastes.&lt;p&gt;I still find that human based recommendations for things like music and movies consistently beat the algorithm at predicting what I&amp;#x27;ll like vs what I won&amp;#x27;t like just about every time.&lt;p&gt;IMHO it will be many years before these recommendation engines consistently pick media I like to watch or listen to.&lt;p&gt;They are still far inferior to human based recommendations.&lt;p&gt;Guessing what someone will want to watch at any given moment is probably a harder problem than getting a car to drive autonomously.&lt;p&gt;EDIT (source): I work on a mostly human powered recommendation engine&amp;#x2F;expert system @ lazyday.tv. It does not use ML to make recommendations. It takes some creative approaches to solving the movie recommendation problem using an expert system and optimizing search results.</text></comment>
<story><title>Categories of deep recommendation systems</title><url>https://jameskle.com/writes/rec-sys-part-2</url></story><parent_chain></parent_chain><comment><author>timkam</author><text>There are recent works that question whether &amp;quot;deep&amp;quot; learning algorithms, and in particular neural networks, are really advancing the state of the art of recommender systems: &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;arxiv.org&amp;#x2F;pdf&amp;#x2F;1907.06902.pdf&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;arxiv.org&amp;#x2F;pdf&amp;#x2F;1907.06902.pdf&lt;/a&gt; (Best Paper Award, RecSys 2019). It is strange that the blog post fails to mention this.</text></comment>