LiNR: Revolutionizing Large-Scale Retrieval for Recommendation Systems
Update: 2024-08-31
Description
The podcast discusses the groundbreaking LiNR system developed by LinkedIn for recommendation engines. LiNR introduces model-based retrieval with attribute-based pre-filtering and quantization techniques to efficiently find and deliver the most relevant content to users.
LiNR's key contributions include model-based retrieval with pre-filtering, quantization techniques for memory optimization, and integration of GPU capabilities. It outperformed traditional systems, leading to significant increases in user interactions, unique users, and content engagement.
Read full paper: https://arxiv.org/abs/2407.13218
Tags: Machine Learning, Information Retrieval, Recommender Systems, Deep Learning, GPU-based Systems
LiNR's key contributions include model-based retrieval with pre-filtering, quantization techniques for memory optimization, and integration of GPU capabilities. It outperformed traditional systems, leading to significant increases in user interactions, unique users, and content engagement.
Read full paper: https://arxiv.org/abs/2407.13218
Tags: Machine Learning, Information Retrieval, Recommender Systems, Deep Learning, GPU-based Systems
Comments
In Channel
Download from Google Play
Download from App Store
United States00:00
00:00
x
