5 citations · 9 across the 8 of their papers we have counts for
4 papers · 1 filter
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…
MultiSlot ReRanker: A Generic Model-based Re-Ranking Framework in Recommendation Systems
Qiang Charles Xiao, Ajith Muralidharan, Birjodh Tiwana +4
In this paper, we propose a generic model-based re-ranking framework, MultiSlot ReRanker, which simultaneously optimizes relevance, diversity, and freshness. Specifically, our Sequ…