On January 16, 2007, Netflix started rolling out a new feature: members could now stream movies directly on their browser without having to wait for the red envelope in the mail. This event marked a substantial shift for Netflix and the entertainment industry. A lot has changed since then. Today, Netflix delivers over 1 billion hours of streaming per month to 48 million members in more than 40 countries. And Netflix accounts for more than a third of peak Internet traffic in the US. This level of engagement results in a humungous amount of data.
At Netflix, we use big data for deep analysis and predictive algorithms to help provide the best experience for our members. A well-known example of this is the personalized movie and show recommendations that are tailored to each member's tastes. The Netflix prize that was launched in 2007 highlighted Netflix's focus on recommendations. Another area that we're focusing on is the streaming quality of experience (QoE), which refers to the user experience once the member hits play on Netflix. This is an area that benefits significantly from data science and algorithms/models built around big data.
Netflix is committed to delivering outstanding streaming service and is investing heavily in advancing the state of the art in adaptive streaming algorithms and network technologies such as Open Connect to optimize streaming quality. Netflix won a Primetime Emmy Engineering Award in 2012 for the streaming service. To put even more focus on "streaming science," we've created a new team at Netflix that's working on innovative approaches for using our data to improve QoE. In this post, I will briefly outline the types of problems we're solving, which include:
- Understanding the impact of QoE on user behavior
- Creating a personalized streaming experience for each member
- Determining what movies and shows to cache on the edge servers based on member viewing behavior
- Improving the technical quality of the content in our catalog using viewing data and member feedback
Understanding the impact of QoE on user behavior
User behavior refers to the way users interact with the Netflix service, and we use our data to both understand and predict behavior. For example, how would a change to our product affect the number of hours that members watch? To improve the streaming experience, we look at QoE metrics that are likely to have an impact on user behavior. One metric of interest is the rebuffer rate, which is a measure of how often playback is temporarily interrupted while more data is downloaded from the server to replenish the local buffer on the client device. Another metric, bitrate, refers to the quality of the picture that is served/seen - a very low bitrate corresponds to a fuzzy picture. There is an interesting relationship between rebuffer rate and bitrate. Since network capacity is limited, picking too high of a bitrate increases the risk of hitting the capacity limit, running out of data in the local buffer, and then pausing playback to refill the buffer. What’s the right tradeoff?
There are many more metrics that can be used to characterize QoE, but the impact that each one has on user behavior, and the tradeoffs between the metrics need to be better understood. More technically, we need to determine a mapping function that can quantify and predict how changes in QoE metrics affect user behavior. Why is this important? Understanding the impact of QoE on user behavior allows us to tailor the algorithms that determine QoE and improve aspects that have significant impact on our members' viewing and enjoyment.
Improving the streaming experience
The Netflix Streaming Supply Chain: opportunities to optimize the streaming experience exist at multiple points
How do we use data to provide the best user experience once a member hits play on Netflix?
Creating a personalized streaming experience
One approach is to look at the algorithms that run in real-time or near real-time once playback has started, which determine what bitrate should be served, what server to download that content from, etc.
With vast amounts of data, the mapping function discussed above can be used to further improve the experience for our members at the aggregate level, and even personalize the streaming experience based on what the function might look like based on each member's "QoE preference." Personalization can also be based on a member's network characteristics, device, location, etc. For example, a member with a high-bandwidth connection on a home network could have very different expectations and experience compared to a member with low bandwidth on a mobile device on a cellular network.
Optimizing content caching
A set of big data problems also exists on the content delivery side. Open Connect is Netflix's own content delivery network that allows ISPs to directly connect to Netflix servers at common internet exchanges, or place a Netflix-provided storage appliance (cache) with Netflix content on it at ISP locations. The key idea here is to locate the content closer (in terms of network hops) to our members to provide a great experience.
One of several interesting problems here is to optimize decisions around content caching on these appliances based on the viewing behavior of the members served. With millions of members, a large catalog, and limited storage capacity, how should the content be cached to ensure that when a member plays a particular movie or show, it is being served out of the local cache/appliance?
Improving content quality
Another approach to improving user experience involves looking at the quality of content, i.e. the video, audio, subtitles, closed captions, etc. that are part of the movie or show. Netflix receives content from the studios in the form of digital assets that are then encoded and quality checked before they go live on the content servers. Given our large and varied catalog that spans several countries and languages, the challenge is to ensure that all our movies and shows are free of quality issues such as incorrect subtitles or captions, our own encoding errors, etc.
In addition to the internal quality checks, we also receive feedback from our members when they discover issues while viewing. This data can be very noisy and may contain non-issues, issues that are not content quality related (for example, network errors encountered due to a poor connection), or general feedback about member tastes and preferences. In essence, identifying issues that are truly content quality related amounts to finding the proverbial needle in a haystack.
By combining member feedback with intrinsic factors related to viewing behavior, we're building models to predict whether a particular piece of content has a quality issue. For instance, we can detect viewing patterns such as sharp drop offs in viewing at certain times during the show and add in information from member feedback to identify problematic content. Machine learning models along with natural language processing (NLP) and text mining techniques can be used to build powerful models to both improve the quality of content that goes live and also use the information provided by our members to close the loop on quality and replace content that does not meet the expectations of Netflix members. As we expand internationally, this problem becomes even more challenging with the addition of new movies and shows to our catalog and the increase in number of languages.
These are just a few examples of ways in which we can use data in creative ways to build models and algorithms that can deliver the perfect viewing experience for each member. There are plenty of other challenges in the streaming space that can benefit from a data science approach. If you're interested in working in this exciting space, please check out the Streaming Science & Algorithms position on the Netflix jobs site.