Machine learning for Netflix recommendations talk at SF Make School
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Faisal Siddiqi from Netflix Personalization Infrastructure team talked about ML at Netflix with a focus on Recommendation use cases
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In this presentation, we survey latent models, starting with shallow and progressing towards deep, as applied to personalization and recommendations. After providing an overview of the Netflix recommender system, we discuss research at the intersection of deep learning, natural language processing and recommender systems and how they relate to traditional collaborative filtering techniques. We will present case studies in the space of deep latent variable models applied to recommender systems.
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For many years, the main goal of the Netflix personalized recommendation system has been to get the right titles in front of our members at the right time. But the job of recommendation does not end there. The homepage should be able to convey to the member enough evidence of why a title may be good for her, especially for shows that the member has never heard of. One way to address this challenge is to personalize the way we portray the titles on our service. An important aspect of how to portray titles is through the artwork or imagery we display to visually represent each title. The artwork may highlight an actor that you recognize, capture an exciting moment like a car chase, or contain a dramatic scene that conveys the essence of a movie or show. It is important to select good artwork because it may be the first time a member becomes aware of a title (and sometimes the only time), so it must speak to them in a meaningful way. In this talk, we will present an approach for personalizing the artwork we use on the Netflix homepage. The system selects an image for each member and video to give better visual evidence for why the title might be appealing to that particular member.
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In this keynote talk we will see how modeling contextual factors such as time or device can help members to find the right content at the right moment
At the end, the goal is to maximize member satisfaction and retention
These slides will go through which contextual factors matters for the video service and why we choose to use them or not.
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For many years, the main goal of the Netflix personalized recommendation system has been to get the right titles in front of our members at the right time. But the job of recommendation does not end there. The homepage should be able to convey to the member enough evidence of why a title may be good for her, especially for shows that the member has never heard of. One way to address this challenge is to personalize the way we portray the titles on our service. An important aspect of how to portray titles is through the artwork or imagery we display to visually represent each title. The artwork may highlight an actor that you recognize, capture an exciting moment like a car chase, or contain a dramatic scene that conveys the essence of a movie or show. It is important to select good artwork because it may be the first time a member becomes aware of a title (and sometimes the only time), so it must speak to them in a meaningful way. In this talk, we will present an approach for personalizing the artwork we use on the Netflix homepage. The system selects an image for each member and video to give better visual evidence for why the title might be appealing to that particular member.
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At Netflix we take context of the member seriously.
In this keynote talk we will see how modeling contextual factors such as time or device can help members to find the right content at the right moment
At the end, the goal is to maximize member satisfaction and retention
These slides will go through which contextual factors matters for the video service and why we choose to use them or not.
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Talk from First Workshop on Recommendation Systems for TV and Online Video at RecSys 2014 in Foster City, CA on 2014-10-10 about how we personalize the layout of the Netflix homepage to make it easier for people to browse the recommendations to quickly find something to watch and enjoy.
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Talk from Software Engineering for Machine Learning Workshop (SW4ML) at the Neural Information Processing Systems (NIPS) 2014 conference in Montreal, Canada on 2014-12-13.
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Netflix provides personalized recommendations at scale to over 37 million members across 40 countries. They take a multi-layered approach using offline, nearline, and online computation. In the offline layer, large datasets are processed to train machine learning models. The nearline layer incrementally refines recommendations based on member events. In the online layer, recommendations are generated and presented to members in real-time based on signals from live services and precomputed results. Netflix recommendations are powered by a massive dataset of over 30 million daily plays and sophisticated algorithms running across distributed cloud computing infrastructure.
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The paper is available here: https://drive.google.com/open?id=1oaM5Fu2bJ0GzMC09yyqjA7eZD9axzSKb
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If you or your organization would like to hire John, please e-mail or call me today. More information at www.JohnDMayfield.com
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- 1. Machine Learning For Recommendations A Netflix Perspective Faisal Siddiqi 9 Feb 2018, Make School, SF @faisalzs
- 2. ?
- 4. ● > 117 million members streaming over 125 million hours a day ● > 190 countries ● > 1000 device types ● Log 100B events/day ● 35.2% of peak US downstream traffic* * Source: https://www.sandvine.com/resources/global-internet-phenomena/2016/north-america-and-latin-america.html Netflix Scale
- 5. Netflix’s History with ML Recommendations (Predict Movie Ratings) 2008 - 2009
- 6. ML @ Netflix Today Recommendations Search Artwork Personalization Content Promotion Artwork Generation Artwork Enhancement Asset Automation Creative Personalization Content Other Popularity Valuation Physical Production Studio in the Cloud Streaming QoE Content Delivery Programmatic Marketing … and many more!
- 7. Turn on Netflix and the best content for you starts playing automatically Recommendations
- 8. Recommendations ● A few seconds to find something great to watch… ● Can only show a few titles ● Enjoyment directly impacts customer satisfaction ● Generates over $1B per year of Netflix revenue ● How? Personalize everything
- 10. Recommendations with Context 2017-12-10 15:40:22 2017-12-23 19:32:10 2017-12-24 12:05:53 2017-12-27 22:40:22 2017-12-29 19:39:36 2017-12-30 20:42:13 Context ActionSequence per user ? Time
- 11. Data from Millions of users Training pipelines Models Precompute System Rankings Online caches AB Test Allocation Systems & Pipelines
- 12. ML and Systems Source: Hidden Technical Debt in Machine Learning Systems
- 13. ExplorationData Models Deploy Training Data Infra Plays Impressions User Actions Store & Serve Adhoc Exploration Infra Notebooks Libraries Frameworks Model Development Infra Orchestration TensorFlow Scikit-learn Hyper Params Production Deployment Infra Continuous Integration Explore/Exploit Logging Feedback
- 15. Takeaways ● Machine Learning is extensively used for Recommendations as well as broadly across several disciplines ● Every pixel, every device, every contact presents an opportunity for personalization ● Solid Infrastructure and Systems is a pre-req for large-scale ML ● Investing in robust software best practices is just as important for Machine Learning as for traditional software development
- 17. Conclusion Netflix is hyper data-driven and a robust experimentation platform facilitates fast and objective decision-making ML innovations allow us to make the Netflix experience highly personalized enabling member joy
Editor's Notes
- Collection of different algorithms that come together to create the complete Netflix experience
- Fresh, Exciting,