5 citations · 9 across the 8 of their papers we have counts for
4 papers · 1 filter
Connected Content Retriever: Dense Graph Edge Features Powering Pre-Ranking at LinkedIn
Akhilesh Gupta, Sudarshan Srinivasa Ramanujam, Chirag Bhanuprasad Mehta +6
In large-scale recommendation systems like the LinkedIn Feed, content generated by a member's network (connections and follows) makes up over 70% of impressions and engagement. It…
LinkedIn Post Embeddings: Industrial Scale Embedding Generation and Usage across LinkedIn
Sudarshan Srinivasa Ramanujam, Akanksha Bindal, Yu Jiang +8
A post embedding (representation of text in embedding space that effectively captures semantic meaning) is a foundational component of LinkedIn that is consumed by product surfaces…
LiGNN: Graph Neural Networks at LinkedIn
Fedor Borisyuk, Shihai He, Yunbo Ouyang +20
In this paper, we present LiGNN, a deployed large-scale Graph Neural Networks (GNNs) Framework. We share our insight on developing and deployment of GNNs at large scale at LinkedIn…
LiRank: Industrial Large Scale Ranking Models at LinkedIn
Fedor Borisyuk, Mingzhou Zhou, Qingquan Song +31
We present LiRank, a large-scale ranking framework at LinkedIn that brings to production state-of-the-art modeling architectures and optimization methods. We unveil several modelin…