3 papers
cs.LG2026
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…
cs.IR2025
Large Scale Retrieval for the LinkedIn Feed using Causal Language Models
Sudarshan Srinivasa Ramanujam, Antonio Alonso, Saurabh Kataria +20
In large scale recommendation systems like the LinkedIn Feed, the retrieval stage is critical for narrowing hundreds of millions of potential candidates to a manageable subset for…
cs.LG2025
From Features to Transformers: Redefining Ranking for Scalable Impact
Fedor Borisyuk, Lars Hertel, Ganesh Parameswaran +14
We present LiGR, a large-scale ranking framework developed at LinkedIn that brings state-of-the-art transformer-based modeling architectures into production. We introduce a modifie…