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Found 8 papers, 1 papers with code
LiMAML: Personalization of Deep Recommender Models via Meta Learning
In the realm of recommender systems, the ubiquitous adoption of deep neural networks has emerged as a dominant paradigm for modeling diverse business objectives.
Learning to Retrieve for Job Matching
Web-scale search systems typically tackle the scalability challenge with a two-step paradigm: retrieval and ranking.
LinkSAGE: Optimizing Job Matching Using Graph Neural Networks
This methodology decouples the training of the GNN model from that of existing Deep Neural Nets (DNN) models, eliminating the need for frequent GNN retraining while maintaining up-to-date graph signals in near realtime, allowing for the effective integration of GNN insights through transfer learning.
LiGNN: Graph Neural Networks at LinkedIn
In this paper, we present LiGNN, a deployed large-scale Graph Neural Networks (GNNs) Framework.
LiRank: Industrial Large Scale Ranking Models at LinkedIn
We present LiRank, a large-scale ranking framework at LinkedIn that brings to production state-of-the-art modeling architectures and optimization methods.
MultiSlot ReRanker: A Generic Model-based Re-Ranking Framework in Recommendation Systems
In this paper, we propose a generic model-based re-ranking framework, MultiSlot ReRanker, which simultaneously optimizes relevance, diversity, and freshness.
NxtPost: User to Post Recommendations in Facebook Groups
In this paper, we present NxtPost, a deployed user-to-post content-based sequential recommender system for Facebook Groups.
Rosetta: Large scale system for text detection and recognition in images
In this paper we present a deployed, scalable optical character recognition (OCR) system, which we call Rosetta, designed to process images uploaded daily at Facebook scale.
Optical Character Recognition
Optical Character Recognition (OCR)
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