Co-authors: Kirill Talanine, Jeffrey D. Gee, Rohan Ramanath, Konstantin Salomatin, Gungor Polatkan, Onkar Dalal, and Deepak Kumar Introduction A common challenge for production machine learning systems is reacting to change. The world can change quickly, particularly on a social network. This can range from sweeping changes at the scale of the whole economy...
relevance Articles
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Co-authors: Ian Ackerman and Saurabh Kataria Editor’s Note: Multi-objective optimization (MOO) is used for many products at LinkedIn (such as the homepage feed) to help balance different behaviors in our ecosystem. There are two parts to how we work with multiple objectives: the first is about training high-fidelity models to predict member behavior (e.g.,...
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Co-authors: Chiachi Lo, Bohong Zhao, and Elina Lin When we launched a major redesign of LinkedIn’s mobile application and desktop web experiences a few years back, we focused the My Network tab to help professionals connect with each other on LinkedIn through our People You May Know feature. Over time, we realized there was much more we could do to help our 706M...
- Topics:
- relevance,
- artificial intelligence,
- Feed Personalization ,
- Data
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Co-authors: Konstantin Salomatin, Kirill Talanine, Barış Özmen, Gungor Polatkan, Linda Fayad, Arjun Kulothungun, and Deepak Kumar Our...
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Co-authors: Sneha Chaudhari, Mahesh Joshi, and Gungor Polatkan In part 1 of this series, we shared a high-level overview of our course...
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Co-authors: Sneha Chaudhari, Mahesh Joshi, and Gungor Polatkan LinkedIn Learning is a platform where LinkedIn members have the...