Speaker
Abstract
LinkedIn's search functionality is one of its oldest capabilities, allowing members to search for people they know, or to discover new connections. With 900 million members on the platform, LinkedIn faces the challenging task for every search you make: who are the most relevant people to show you?
Unlike other recommendation systems, machine learning systems contain one key component not found elsewhere: the query. User intent is textually explicit through the query, and search results must conform accordingly.
In this presentation, we'll explore how LinkedIn's People Search system uses ML to surface the right person that you're looking for, including but not limited to:
1. Retrieval: determining the profiles relevant to your search intent
2. Ranking: selecting the most relevant profiles to show you
Expertise in ML is not a pre-requisite to enjoy this presentation, and while some background is helpful, all are encouraged to attend.
Topics
QCon New York 2023 is a three day conference for senior software engineers, architects and team leads. An international program committee of working engineers selects every session. Patterns and practices, not products and pitches.
Part of the track
ML in Practice Hosted by Sid Anand Chief Architect and Head of Engineering @DatazoomFrom the same track
Thursday 15 June
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