Events and Updates

Curious what events and conferences we're attending? Find out here.

Recent Updates

2018 Workshop on Personalization, Recommendation and Search

Jun 20, 2018

The Netflix Research team hosted our annual Personalization, Recommendation and Search (PRS) workshop on June 8th. This workshop has been running for the last three years (2017 program, 2016 program) and aims at bringing together practitioners and researchers in these three domains, to facilitate the sharing of information and practices. We had around 250 participants from a wide range of companies and institutions, as well as 8 invited talks throughout the day:

  • Henriette Cramer (Spotify) -- Pragmatic lessons learnt while teaching machines
  • Anoop Deoras and Dawen Liang (Netflix) -- Latent Models, Shallow and Deep, for Recommender Systems
  • Evangelia Christakopoulou (UMN) -- Improving the quality of top-N recommendation: modern approaches with a special focus on similar users' behavior
  • Ed H. Chi (Google) -- Beyond Being Accurate: Toward More Inclusive and Fairer Models using Focused Learning and Adversarial Training
  • Suju Rajan (Criteo) -- Recommender Systems in an Real Time Bidding Platform
  • Jaya Kawale and Fernando Amat (Netflix) -- Multi-armed Bandit Approaches for recommendations at Netflix
  • Kristi Schneck (Pandora) -- Personalized Concert Recommendations at Pandora
  • Adith Swaminathan (Microsoft) -- Learning from logged bandit feedback

We'll be uploading the slides over the next few days on the workshop website.

Streaming Science & Analytics Event

Mar 7, 2018

The Streaming Science & Analytics team hosted a meetup at Netflix on March 6, 2018 to highlight data science and analytics work to optimize the Netflix streaming experience. The team presented 5 lightning talks to a group of local data science, analytics, and streaming video experts. It was a great opportunity for us to share some of our work and network with a strong group of local experts.

  • Nirmal Govind provided an overview of the history of streaming science at Netflix and the team’s mission: optimizing the streaming video quality of experience (QoE).
  • Laura Pruitt discussed the Netflix “Aim Low” initiative to provide great streaming quality in developing markets.
  • Andrew Berglund presented data science and algorithmic challenges associated with the Netflix content delivery network (Open Connect): how do we efficiently deliver so much high quality video over the internet?
  • Chaitu Ekanadham presented challenges and opportunities for applying cutting-edge Machine Learning techniques to optimize adaptive bitrate streaming.
  • Martin Tingley described how we use AB testing and causal inference to measure the impact of algorithm or hardware changes on streaming QoE, and to understand member preferences between QoE trade-offs.