At LinkedIn, we face a number of challenges in delivering high quality search results to 277M+ members. Our results are highly personalized, requiring us to build machine-learned relevance models that combine document, query, and user features. And our emphasis on entities (names, companies, job titles, etc.) affects how we process and understand queries. In this talk, we’ll talk about these challenges in detail, and we’ll describe some of the solutions we are building to address them.
Satya Kanduri has worked on LinkedIn search relevance since 2011. Most recently he led the development of LinkedIn’s machine-learned ranking platform. He previously worked at Microsoft, improving relevance for Bing Product Search. He has an MS in Computer Science from the University of Nebraska - Lincoln, and a BE in Computer Science from the Osmania University College of Engineering.
Abhimanyu Lad has worked at LinkedIn as a software engineer and data scientist since 2011. He has worked on a variety of relevance and query understanding problems, including query intent prediction, query suggestion, and spelling correction. He has a PhD in Computer Science from CMU, where he worked on developing machine learning techniques for diversifying search results.