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Why Your Search Relevance Project Will Fail

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Summer Marathon. ML in Production, Day 4 - Doug Turnbull

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We should just feed our user’s clicks into an ML model, teaching it to rank what’s clicked more... Right? Well not so fast! Transforming search clickstream data into usable training data can be a daunting task. Without careful, systematic study, we can easily create a negative feedback loop where clicks strengthen the existing poor search experience.

I’ve led and participated in dozens of search relevance projects. Poor training data is the #1 reason relevance and machine learning projects fail.

In this talk, I’ll cover the core biases in search clickstream data and what to do about it. You’ll see how to systematically overcome biases due to low traffic and how you display results. With a good foundation in place, then the sky is the limit for how much you can grow, tune, or machine-learn relevance: having an impact beyond the test/training split!

This is Day #4 for the "ML in Production" track of our summer marathon conference. More information here: https://datatalks.club/conferences/2021-summer-marathon.html

About the speaker:

In 2012, Doug saw search relevance would be central to user experiences. Sadly, search relevance was a topic clouded esoteric mystery. Doug began to democratize this daunting field through blogging and speaking. In 2016, Doug’s book Relevant Search woke the industry up to the importance of search quality. Doug now works at Shopify, working to make great commerce search+discovery possible for everyone. Doug co-created Elasticsearch Learning to Rank with the Wikimedia Foundation and contributed to Trey Grainger’s upcoming AI Powered Search. You can find Doug at his website where he blogs about search and data.

DataTalks.Club is the place to talk about data. Join our slack community!

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