At LinkedIn, our engineering teams are constantly working to keep the company at the cutting edge of innovation to deliver value for our members and customers. We recognize that many innovations are happening within academia and partnering more closely with them will strengthen our ability to research, ideate and accelerate the value we are able to deliver to...
Recommender Systems Articles
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- Topics:
- artificial intelligence,
- Recommender Systems,
- nlp
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Co-authors: Adam Leon and David Golland At LinkedIn, relationships matter. On our platform, we focus on helping our members build and maintain the relationships they have formed throughout their professional career. In LinkedIn Messaging, our members form new and reunite with established communities from their network through group chats - Messaging’s...
- Topics:
- Recommender Systems
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Co-authors: Rupesh Gupta, Sasha Ovsankin, Qing Li, Seunghyun Lee, Benjamin Le, and Sunil Khanal At LinkedIn, we strive to serve the most relevant recommendations to our members, whether that’s a job they may be interested in, a member they may want to connect with, or another type of suggestion. In order to do that, we need to know their intent and preferences,...
- Topics:
- Recommendations,
- Apache Samza,
- Recommender Systems
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Co-authors: Yunbo Ouyang,Viral Gupta, Kinjal Basu, Cyrus Diciccio, Brendan Gavin, and Lin Guo. Most large-scale recommender systems...
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Co-authors: Kirill Talanine, Jeffrey D. Gee, Rohan Ramanath, Konstantin Salomatin, Gungor Polatkan, Onkar Dalal, and Deepak Kumar...
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Co-authors: Qiannan Yin, Yan Wang, Divya Venugopalan, Cyrus Diciccio, Heloise Logan, Preetam Nandy, Kinjal Basu, and Albert Cui...