TECH BLOG
TECH BLOG
Hulu Tech Blog
Applying Deep Learning to Collaborative Filtering: How Hulu builds its industry leading recommendation engine
Yin Zheng • Aug 1, 2016
Researchers at Hulu invented a novel neural network based collaborative filtering approach, called neural autoregressive distribution estimator for collaborative filtering (CF-NADE), which is state-of-the-art on several challenging public benchmarks.
Tags: Collaborative Filtering, Deep Learning, Neural Network, Recommendation System,
Creating Hulu VR
Julian Eggebrecht • Mar 24, 2016
Hulu is passionate about the art of great storytelling and evolving that art of storytelling in TV and movies. Hulu sits right at the intersection of entertainment and technology, so it was only natural to explore how Hulu could bring the exciting virtual reality platform to viewers.
Tags: VR,
Monaco - Efficiently Hosting 1.5TB of Redis Servers
Keith Ainsworth • Oct 6, 2015
Redis has become an indispensable technology at Hulu as we continue to scale and innovate new features and products. Redis is a lightning-fast in memory key-object database with a very light hardware footprint, which makes it ideal for building new projects. It was so ideal that we found, in our infrastructure, dozens of virtual machines (VM's) and bare-metal servers dedicated entirely to hosting a redis-server.
Tags: Monaco, Redis,
DNS infrastructure at Hulu.
Kirill Timofeev • Sep 8, 2015
For companies running their own datacenter, setting up internal DNS infrastructure is essential for performance and ease of maintenance. Setting up a single DNS server for occasional requests is pretty straightforward, but scaling and distributing requests across multiple data centers is challenging. In this post, we'll describe the evolution of our DNS infrastructure from a simple setup to a more distributed configuration that is capable of reliably handling a significantly higher request volume.
Tags: DNS,
Voidbox - Docker on YARN
Huahui Yang • Aug 6, 2015
YARN is the distributed resource management system in Hadoop 2.0, which is able to schedule cluster resources for diverse high-level applications such as MapReduce, Spark. However, nowadays, all existing framework on top of YARN are designed with assumption of specific system environment. How to support user applications with arbitrary complex environment dependencies is still an open question. Docker gives the answer.
Tags: Docker, YARN,
You Can Now Use the Apple Watch as a Hulu Remote
Rolla Selbak • Jul 15, 2015
Hulu is passionate about the art of great storytelling and evolving that art of storytelling in TV and movies. Hulu sits right at the intersection of entertainment and technology, so it was only natural to explore how Hulu could bring the exciting virtual reality platform to viewers.
Tags: Apple Watch, Content,
Aggregation of Relevance Tables with Expert Labeling
Heng Su • May 26, 2015
The simplest and maybe the most intuitive way to aggregate the sub relevance tables is to manually evaluate the quality and assign a weight for each relevance table, then we can just do weighted linear combination of those relevance tables to generate the result. However apparently this is not good enough. First the quality of the relevance tables will change when they update; second this global model is not the best to capture all the useful information in those relevance tables.
Tags: Relevance table, Recommendation Service,
Face Match System - Clustering, Recognition and Summary
Cailiang Liu • May 4, 2014
there is a series of face tracks have been extracted from a set of videos, the next step is to tag them automatically with some probable actor names from the given show. After all, manually processing all the tracks from scratch would be infeasible. The tags, with some sort of acceptable accuracy rate let's say 80 percent provide valuable cues for a person to verify the tracks in groups. When presented in a user-friendly interface, the tags also improve the speed required to correct erroneous matches. In this blog entry, we refer to this problem of automatically annotating faces (not tracks) as face tagging.
Tags: Face Match, Face Recognition,