Serving the most relevant information for LinkedIn members in the homepage feed requires a massive effort—hundreds of features are used to personalize content for hundreds of millions of members. For each homepage visit, our machine learning models have to find and surface the best activity across a member's whole network, and they have to source that content...
machine learning Articles
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Co-authors: Guillaume Saint-Jacques, Amir Sepehri, Nicole Li, and Igor Perisic Introduction Previously on this blog, we’ve shared information on best practices in data science, particularly in areas such as A/B testing. We’ve also discussed the importance of ethics in fields such as data science, early implementations of “fairness by design” principles in our...
- Topics:
- A/B Testing,
- data science,
- experimentation,
- research,
- Product Design,
- Data,
- machine learning,
- T-REX
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Our members place their trust in us, and expect and deserve a safe and trusted community where they can express themselves professionally. We are constantly investing in a variety of systems (Automated Fake Account Detection at LinkedIn, Defending Against Abuse at LinkedIn’s Scale, and How We’re Protecting Members From Fake Profiles) to help automatically detect...
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The Anti-Abuse AI Team at LinkedIn creates, deploys, and maintains models that detect and prevent various types of abuse, including...
- Topics:
- machine learning,
- Open Source,
- trust engineering
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Co-authors: Stuart Ambler, Ahsan Chudhary, Mark Dietz, Sahin Cem Geyik, Krishnaram Kenthapadi, Ian Koeppe, Varun Mithal, Guillaume...
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Co-authors: Ali Mohamed and Zheng Li LinkedIn’s feed stands at the center of building global professional knowledge-sharing...