Beyond The Search Engine: Improving Relevancy through Query Expansion

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Due to a variable inventory and an ephemeral data set, users often search for terms that are outside of our corpus. This leads to empty search result sets, despite often having relevant content for our users.

In order to improve relevancy, we moved beyond the search engine and implemented a number of Query Expansion techniques, including spell correction, category identification and synonym matching.

In this talk, we will outline how we used machine learning and heuristics to improve the search experience for our users while highlighting successes and failures along the way.

Taylor Rose

Ibotta

Taylor is a Senior Software Engineer on the search team at Ibotta, a Denver-based online shopping rewards platform. He began his career doing full-stack generalist development for startups but took an interest in the search space at Ibotta. As the company grew, browsing content became impossible without a fully featured search engine. He's spent the last two years absorbing search knowledge and applying it to Ibotta's unique search problem.