5 citations · 9 across the 8 of their papers we have counts for
9 papers
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…
An Industrial-Scale Sequential Recommender for LinkedIn Feed Ranking
Lars Hertel, Gaurav Srivastava, Syed Ali Naqvi +21
LinkedIn Feed enables professionals worldwide to discover relevant content, build connections, and share knowledge at scale. We present Feed Sequential Recommender (Feed SR), a tra…
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…
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…
Measuring Long-term Impact of Ads on LinkedIn Feed
Jinyun Yan, Birjodh Tiwana, Souvik Ghosh +2
Organic updates (from a member's network) and sponsored updates (or ads, from advertisers) together form the newsfeed on LinkedIn. The newsfeed, the default homepage for members, a…
Personalized Treatment Selection using Causal Heterogeneity
Ye Tu, Kinjal Basu, Cyrus DiCiccio +4
Randomized experimentation (also known as A/B testing or bucket testing) is widely used in the internet industry to measure the metric impact obtained by different treatment varian…