At Netflix we take context of the member seriously.

In this keynote talk we will see how modeling contextual factors...

Contextual bandits are a common modeling tool in web-scale personalization systems such as entertainment recommendations and ad ranking. Usually, new algorithm variants are evaluated online via A/B testing. However, to reduce the number of variants tested online, it’s crucial to have reliable metrics to test policies offline, using data obtained from production policies – a technique known as off-policy evaluation. One of...

We are interested in Bayesian models for collaborative filtering that incorporate side-information or metadata about items in addition to user-item interaction data. We present a simple and flexible framework to build models for this task that exploit the low-rank structure in user-item interaction datasets. Although the resulting models are non-conjugate, we develop an efficient technique for approximating posteriors over...

Another day, another custom script to analyze an A/B test. Maybe you’ve done this before and have an old script lying...

We introduce a new Thompson sampling-based algorithm, called marginal posterior sampling, for online slate bandits, that is characterized by three key ideas. First, it postulates that the slate-level reward is a monotone function of the marginal unobserved rewards of the base actions selected in the slates’s slots, but it does not attempt to estimate this function. Second, instead of maintaining a slate-level reward...

This blog post describes our approach to predictive CPU isolation for containers at Netflix -- enabling tasks to run...

Invited talk at the Adaptive and Multi-Task Learning (AMTL) workshop at ICML 2019 on 2019-06-15 in Long Beach, CA....

What's deeper than deep learning? A look at how recommender systems have grown from matrix factorization, to deep...

Both router buffer sizing and Active Queue Management (AQM) are concepts deeply embedded inside the computer...

In this talk our own Sudeep Das gives a high level description of how personalization helps our members find relevant...

Providing high-fidelity, near real-time data is important for multi-armed bandit and RL models to converge quickly....

We discuss salient challenges of building a search experience for a streaming media service such as Netflix. We provide an overview of the role of recommendations within the search context to aid content discovery and support searches for unavailable (out-of-catalog) entities. We also stress the importance of keystroke-level Instant Search experience, and the technical challenges associated with implementing it across...

Learn more about how Netflix uses data science and analytics to improve the reliability, security and efficiency of...

In order to produce software that improves a scientist's productivity we have come up with the following design...

From living rooms in Bogota, to morning commutes in Tokyo, to beaches in Los Angeles and dorms in Berlin, Netflix...

Netflix has over 139M members streaming on more than half a billion devices spanning over 1,700 different types of devices from hundreds of brands. This diverse device ecosystem results in a high dimensionality feature space, often with sparse data, and can make identifying device performance issues challenging.

Describes the latest progress towards Netflix's open-source video quality assessment metric.

We’re developing a new product within the wider Experimentation Platform, to automate some aspects of the scientists...