The Anti-Abuse AI Team at LinkedIn creates, deploys, and maintains models that detect and prevent various types of abuse, including the creation of fake accounts, member profile scraping, automated spam, and account takeovers. There are several unique challenges we face when using machine learning to prevent abuse on a large professional network, including:...
machine learning Articles
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- Topics:
- machine learning,
- Open Source
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Co-authors: Stuart Ambler, Ahsan Chudhary, Mark Dietz, Sahin Cem Geyik, Krishnaram Kenthapadi, Ian Koeppe, Varun Mithal, Guillaume Saint-Jacques, Amir Sepehri, Thanh Tran, and Sriram Vasudevan Editor’s note: A shorter version of this article was originally posted by Krishnaram Kenthapadi on LinkedIn. Introduction How do we take fairness and transparency into...
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Co-authors: Ali Mohamed and Zheng Li LinkedIn’s feed stands at the center of building global professional knowledge-sharing communities for our members. Members talk about their career stories, job openings, and ideas in a variety of formats, including links, video, text, images, documents, and long-form articles. Members participate in conversations in two...
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“Make LinkedIn feel instantaneous” is a mantra our performance team lives by to deliver the best possible experience to our members....
- Topics:
- Performance,
- experimentation,
- machine learning
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Co-authors: Burcu Baran, Xiaojing Dong, Chi-Yi Kuan, Emily Huang, and Tiger Zhang At LinkedIn, we have more than 630M members, 30M...
- Topics:
- data science,
- machine learning,
- metrics,
- events
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As members explore the many products of LinkedIn, including the feed, homepage, Learning, Recruiter, and Sales Navigator, to name a...