User signals (clicks, purchases, etc.) are among the most useful inputs for improving search relevance. They can be used to directly optimize your head queries (signals boosting), to personalize search results, to learn domain-specific terminology (misspellings, synonyms, etc.), or to build click models as training data for automated Learning to Rank. Most organizations struggle to properly store their signals, let alone best utilize them to optimize relevance. In this talk, you’ll learn best practices for collecting, processing, and applying signals to enhance relevance. We cover live code examples of index- and query-time signals boosting, fighting signal spam and bias, and applying quality- and time-based weights to your models. We show the various kinds of personalization and click models you can train from signals to improve ranking. You'll come away from this talk with some new tools in your relevance engineering toolbox, and some open-source code examples to get started!
Trey Grainger is CTO at Presearch, the decentralized web search engine, and is the Founder of Searchkernel, a startup helping clients build next-generation, intelligent search applications. He is the author of the books AI-Powered Search and Solr in Action, and is the former Chief Algorithms Officer and SVP of Engineering at Lucidworks, a leading AI-powered search company. He studied information retrieval and web search at Stanford University, received his Masters in Management of Technology from Georgia Tech, and received his Bachelors degree from Furman University in Computer Science, Business, and Philosophy.
