Node at LinkedIn: The Pursuit of Thinner, Lighter, Faster

Better, faster, lighter Java

Image Semantic Transformation: Faster, Lighter and Stronger

We propose Image-Semantic-Transformation-ReconstructionCircle(ISTRC) model, a novel and powerful method using facenet’s Euclidean latent space to understand the images. As the name suggests, ISTRC construct the circle, able to perfectly reconstruct images. One powerful Euclidean latent space embedded in ISTRC is FaceNet’s last layer with the power of distinguishing and understanding images. Our...

The Browsemaps: Collaborative Filtering at LinkedIn

Many web properties make extensive use of item-based collaborative filtering, which showcases relationships between pairs of items based on the wisdom of the crowd. This paper presents LinkedIn’s horizontal collaborative filtering infrastructure, known as browsemaps. The platform enables rapid development, deployment, and computation of collaborative filtering recommendations for almost any use...

Faster and Lighter Phrase-based Machine Translation Baseline

This paper describes the SENSE machine translation system participation in the Third Workshop for Asian Translation (WAT2016). We share our best practices to build a fast and light phrasebased machine translation (PBMT) models that have comparable results to the baseline systems provided by the organizers. As Neural Machine Translation (NMT) overtakes PBMT as the state-of-the-art, deep learning...

Stateful Scalable Stream Processing at LinkedIn

Distributed stream processing systems need to support stateful processing, recover quickly from failures to resume such processing, and reprocess an entire data stream quickly. We present Apache Samza, a distributed system for stateful and fault-tolerant stream processing. Samza utilizes a partitioned local state along with a low-overhead background changelog mechanism, allowing it to scale to ...