Needle in a 930M Member Haystack: People Search AI @LinkedIn

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. 


Speaker

Mathew Teoh

Machine Learning @ LinkedIn

Mat is passionate about helping others find what they need.
 
As an ML engineer at LinkedIn, he leads the technical development of the ML behind People Search, LinkedIn's search engine that helps members find other people that are interesting to them. Before that, he built the NLP system at brain.ai, an early-stage startup that helps users shop by simply saying what they need. Before that, he worked as a Data Scientist at Quora, analyzing experiments that helped users find answers to their questions.
 
When he is not finding local minima in high-dimensional spaces, Mat enjoys finding local minima in his ski boots, or local maxima in his hiking boots.

Read more
Find Mathew Teoh at:

Date

Thursday Jun 15 / 11:50AM EDT ( 50 minutes )

Location

Dumbo / Navy Yard

Topics

Search AI/ML Recommendations Ranking Social Networks

Share

From the same track

Session ML in Practice

Back to Basics: Scalable, Portable ML in Pure SQL

Thursday Jun 15 / 02:55PM EDT

Redshift has SageMaker. BigQuery begat BigML. Spark birthed Databricks. Every data warehouse is tightly coupled to a particular ML stack.

Speaker image - Evan Miller

Evan Miller

Principal Statistics Engineer @Eppo (Creator of Evan's Awesome A/B Tools)

Session AI/ML

PostgresML: Leveraging Postgres as a Vector Database for AI

Thursday Jun 15 / 10:35AM EDT

With the growing importance of AI and machine learning in modern applications, data scientists and developers are constantly exploring new and efficient ways to store and analyze large amounts of data.

Speaker image - Montana Low

Montana Low

Machine Learning w/ PostgresML

Session AI/ML

Going Beyond the Case of Black Box AutoML

Thursday Jun 15 / 01:40PM EDT

Most AutoML tools are black-box tools. They offer no code/low code tools (UI/simple APIs) for practitioners to get started quickly. While this helps beginners, most experienced data scientists/ML practitioners often need more control.

Speaker image - Kiran Kate

Kiran Kate

Senior Technical Staff Member @IBM Research

Session

LLMs in the Real World: Structuring Text with Declarative NLP

Thursday Jun 15 / 04:10PM EDT

Building machine learning pipelines to extract structured data from unstructured text is a popular problem within an unpopular development lifecycle.

Speaker image - Adam Azzam

Adam Azzam

AI Product Lead @Prefect