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Learning to Rank for Reddit Search: A Project Retro

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Learning to Rank for Reddit Search: A Project Retro
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64
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CC Attribution 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
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In today’s AI based world, Reddit stands out as a deep catalog of human, subjective information. Whether product reviews or the deeply personal - Reddit searchers want to connect with other humans, not generic AI based answers. We at Reddit would like the site-search experience to be better, so you don’t need to add “Reddit” to your Google search. That’s what we’re trying to do with Learning to Rank: turning relevance into a repeatable, data-driven solution. The journey hasn’t been an easy one. We want to share our painful lessons learned working with training data, developing features, the Solr Learning to Rank plugin, scaling Learning to Rank to 1000s of QPS, and more. Hopefully, you can learn from the egg we constantly found on our faces! See how our scrappy, understaffed team has been slowly turning LTR from a science project into a repeatable process of constant, data-informed improvement. From a lab to an assembly line, come and learn from our painful lessons big and small.