Deconstructing how Netflix got success thanks to a heavily personalized user experience. After the ten findings, there is a set of checklists and examples using ContentWise on how to apply the lessons to add personalization to a video service. For marketers, UI designers, multiscreen developers, TV executives and systems integrators.
Personalization at Netflix - Making Stories Travel Sudeep Das, Ph.D.
I give a high level overview of how personalization at Netflix helps our members find titles that spark joy, as well as help stories travel across the world.
Tutorial on Deep Learning in Recommender System, Lars summer school 2019Anoop Deoras
I had a fun time giving tutorial on the topic of deep learning in recommender systems at Latin America School on Recommender Systems (LARS) in Fortaleza, Brazil.
Personalizing "The Netflix Experience" with Deep LearningAnoop Deoras
These are the slides from my talk presented at AI Next Con conference in Seattle in Jan 2019. Here I talk in a bit more detail about the intuition behind collaborative filtering and go a bit deeper into the details of non linear deep learned models.
Deeper Things: How Netflix Leverages Deep Learning in Recommendations and Se...Sudeep Das, Ph.D.
In this talk, we will provide an overview of Deep Learning methods applied to personalization and search at Netflix. We will set the stage by describing the unique challenges faced at Netflix in the areas of recommendations and information retrieval. Then we will delve into how we leverage a blend of traditional algorithms and emergent deep learning methods and new types of embeddings, especially hyperbolic space embeddings, to address these challenges.
Presentation at the Netflix Expo session at RecSys 2020 virtual conference on 2020-09-24. It provides an overview of recommendation and personalization at Netflix and then highlights some of the things we’ve been working on as well as some important open research questions in the field of recommendations.
Talk with Yves Raimond at the GPU Tech Conference on Marth 28, 2018 in San Jose, CA.
Abstract:
In this talk, we will survey how Deep Learning methods can be applied to personalization and recommendations. We will cover why standard Deep Learning approaches don't perform better than typical collaborative filtering techniques. Then we will survey we will go over recently published research at the intersection of Deep Learning and recommender systems, looking at how they integrate new types of data, explore new models, or change the recommendation problem statement. We will also highlight some of the ways that neural networks are used at Netflix and how we can use GPUs to train recommender systems. Finally, we will highlight promising new directions in this space.
Personalization at Netflix - Making Stories Travel Sudeep Das, Ph.D.
I give a high level overview of how personalization at Netflix helps our members find titles that spark joy, as well as help stories travel across the world.
Tutorial on Deep Learning in Recommender System, Lars summer school 2019Anoop Deoras
I had a fun time giving tutorial on the topic of deep learning in recommender systems at Latin America School on Recommender Systems (LARS) in Fortaleza, Brazil.
Personalizing "The Netflix Experience" with Deep LearningAnoop Deoras
These are the slides from my talk presented at AI Next Con conference in Seattle in Jan 2019. Here I talk in a bit more detail about the intuition behind collaborative filtering and go a bit deeper into the details of non linear deep learned models.
Deeper Things: How Netflix Leverages Deep Learning in Recommendations and Se...Sudeep Das, Ph.D.
In this talk, we will provide an overview of Deep Learning methods applied to personalization and search at Netflix. We will set the stage by describing the unique challenges faced at Netflix in the areas of recommendations and information retrieval. Then we will delve into how we leverage a blend of traditional algorithms and emergent deep learning methods and new types of embeddings, especially hyperbolic space embeddings, to address these challenges.
Presentation at the Netflix Expo session at RecSys 2020 virtual conference on 2020-09-24. It provides an overview of recommendation and personalization at Netflix and then highlights some of the things we’ve been working on as well as some important open research questions in the field of recommendations.
Talk with Yves Raimond at the GPU Tech Conference on Marth 28, 2018 in San Jose, CA.
Abstract:
In this talk, we will survey how Deep Learning methods can be applied to personalization and recommendations. We will cover why standard Deep Learning approaches don't perform better than typical collaborative filtering techniques. Then we will survey we will go over recently published research at the intersection of Deep Learning and recommender systems, looking at how they integrate new types of data, explore new models, or change the recommendation problem statement. We will also highlight some of the ways that neural networks are used at Netflix and how we can use GPUs to train recommender systems. Finally, we will highlight promising new directions in this space.
Talk from QCon SF on 2018-11-05
For many years, the main goal of the Netflix personalized recommendation system has been to get the right titles in front each of our members at the right time. With a catalog spanning thousands of titles and a diverse member base spanning over a hundred million accounts, recommending the titles that are just right for each member is crucial. But the job of recommendation does not end there. Why should you care about any particular title we recommend? What can we say about a new and unfamiliar title that will pique your interest? How do we convince you that a title is worth watching? Answering these questions is critical in helping our members discover great content, especially for unfamiliar titles. One way to do this is to consider the artwork or imagery we use to visually portray each title. If the artwork representing a title captures something compelling to you, then it acts as a gateway into that title and gives you some visual “evidence” for why the title might be good for you. Selecting good artwork is important 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 show for each title on the Netflix homepage. We will look at how to frame this as a machine learning problem using contextual multi-armed bandits in a recommendation system setting. We will also describe the algorithmic and system challenges involved in getting this type of approach for artwork personalization to succeed at Netflix scale. Finally, we will discuss some of the future opportunities that we see to expand and improve upon this approach.
Déjà Vu: The Importance of Time and Causality in Recommender SystemsJustin Basilico
Talk at RecSys 2017 in Como, Italy on 2017-08-29.
Abstract:
Time plays a key role in recommendation. Handling it properly is especially critical when using recommender systems in real-world applications, which may not be as clear when doing research with historical data. In this talk, we will discuss some of the important challenges of handling time in recommendation algorithms at Netflix. We will focus on challenges related to how our users, items, and systems all change over time. We will then discuss some strategies for tackling these challenges, which revolves around proper treatment of causality in our systems.
Shallow and Deep Latent Models for Recommender SystemAnoop Deoras
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.
Personalized Page Generation for Browsing RecommendationsJustin Basilico
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.
(Presented at the Deep Learning Re-Work SF Summit on 01/25/2018)
In this talk, we go through the traditional recommendation systems set-up, and show that deep learning approaches in that set-up don't bring a lot of extra value. We then focus on different ways to leverage these techniques, most of which relying on breaking away from that traditional set-up; through providing additional data to your recommendation algorithm, modeling different facets of user/item interactions, and most importantly re-framing the recommendation problem itself. In particular we show a few results obtained by casting the problem as a contextual sequence prediction task, and using it to model time (a very important dimension in most recommendation systems).
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.
Past, Present & Future of Recommender Systems: An Industry PerspectiveJustin Basilico
Slides from our talk at the RecSys 2016 conference in Boston, MA 2016-09-18 on our perspective for what are important areas for future work in recommender systems.
From Idea to Execution: Spotify's Discover WeeklyChris Johnson
Discover Weekly is a personalized mixtape of 30 highly personalized songs that's curated and delivered to Spotify's 75M active users every Monday. It's received high acclaim in the press and reached 1B streams within its first 10 weeks. In this slide deck we dive into the narrative of how Discover Weekly came to be, highlighting technical challenges, data driven development, and the Machine Learning models used to power our recommendations engine.
Crafting Recommenders: the Shallow and the Deep of it! Sudeep Das, Ph.D.
I present a brief review, and an outlook on the rapid changes happening in the field of recommendation engine research on the heels of the deep learning revolution!
Netflix is the world’s leading Internet television network with over 48 million members in more than 40 countries enjoying more than one billion hours of TV shows and movies per month, including original series. Netflix uses machine learning to deliver a personalized experience to each one of our 48 million users.
In this talk you will hear about the machine learning algorithms that power almost every part of the Netflix experience, including some of our recent work on distributed Neural Networks on AWS GPUs. You will also get an insight into the innovation approach that includes offline experimentation and online AB testing. Finally, you will learn about the system architectures that enable all of this at a Netflix scale.
by Harald Steck (Netflix Inc., US), Roelof van Zwol (Netflix Inc., US) and Chris Johnson (Spotify Inc., US)
Slides of the tutorial on interactive recommender systems at the 2015 conference on Recommender Systems (RecSys).
Interactive recommender systems enable the user to steer the received recommendations in the desired direction through explicit interaction with the system. In the larger ecosystem of recommender systems used on a website, it is positioned between a lean-back recommendation experience and an active search for a specific piece of content. Besides this aspect, we will discuss several parts that are especially important for interactive recommender systems, including the following: design of the user interface and its tight integration with the algorithm in the back-end; computational efficiency of the recommender algorithm; as well as choosing the right balance between exploiting the feedback from the user as to provide relevant recommendations, and enabling the user to explore the catalog and steer the recommendations in the desired direction.
In particular, we will explore the field of interactive video and music recommendations and their application at Netflix and Spotify. We outline some of the user-experiences built, and discuss the approaches followed to tackle the various aspects of interactive recommendations. We present our insights from user studies and A/B tests.
The tutorial targets researchers and practitioners in the field of recommender systems, and will give the participants a unique opportunity to learn about the various aspects of interactive recommender systems in the video and music domain. The tutorial assumes familiarity with the common methods of recommender systems.
DATE: Wednesday, Sept 16, 2015, 11:00-12:30
A Multi-Armed Bandit Framework For Recommendations at NetflixJaya Kawale
In this talk, we present a general multi-armed bandit framework for recommendations on the Netflix homepage. We present two example case studies using MABs at Netflix - a) Artwork Personalization to recommend personalized visuals for each of our members for the different titles and b) Billboard recommendation to recommend the right title to be watched on the Billboard.
Talk from QCon SF on 2018-11-05
For many years, the main goal of the Netflix personalized recommendation system has been to get the right titles in front each of our members at the right time. With a catalog spanning thousands of titles and a diverse member base spanning over a hundred million accounts, recommending the titles that are just right for each member is crucial. But the job of recommendation does not end there. Why should you care about any particular title we recommend? What can we say about a new and unfamiliar title that will pique your interest? How do we convince you that a title is worth watching? Answering these questions is critical in helping our members discover great content, especially for unfamiliar titles. One way to do this is to consider the artwork or imagery we use to visually portray each title. If the artwork representing a title captures something compelling to you, then it acts as a gateway into that title and gives you some visual “evidence” for why the title might be good for you. Selecting good artwork is important 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 show for each title on the Netflix homepage. We will look at how to frame this as a machine learning problem using contextual multi-armed bandits in a recommendation system setting. We will also describe the algorithmic and system challenges involved in getting this type of approach for artwork personalization to succeed at Netflix scale. Finally, we will discuss some of the future opportunities that we see to expand and improve upon this approach.
Déjà Vu: The Importance of Time and Causality in Recommender SystemsJustin Basilico
Talk at RecSys 2017 in Como, Italy on 2017-08-29.
Abstract:
Time plays a key role in recommendation. Handling it properly is especially critical when using recommender systems in real-world applications, which may not be as clear when doing research with historical data. In this talk, we will discuss some of the important challenges of handling time in recommendation algorithms at Netflix. We will focus on challenges related to how our users, items, and systems all change over time. We will then discuss some strategies for tackling these challenges, which revolves around proper treatment of causality in our systems.
Shallow and Deep Latent Models for Recommender SystemAnoop Deoras
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.
Personalized Page Generation for Browsing RecommendationsJustin Basilico
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.
(Presented at the Deep Learning Re-Work SF Summit on 01/25/2018)
In this talk, we go through the traditional recommendation systems set-up, and show that deep learning approaches in that set-up don't bring a lot of extra value. We then focus on different ways to leverage these techniques, most of which relying on breaking away from that traditional set-up; through providing additional data to your recommendation algorithm, modeling different facets of user/item interactions, and most importantly re-framing the recommendation problem itself. In particular we show a few results obtained by casting the problem as a contextual sequence prediction task, and using it to model time (a very important dimension in most recommendation systems).
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.
Past, Present & Future of Recommender Systems: An Industry PerspectiveJustin Basilico
Slides from our talk at the RecSys 2016 conference in Boston, MA 2016-09-18 on our perspective for what are important areas for future work in recommender systems.
From Idea to Execution: Spotify's Discover WeeklyChris Johnson
Discover Weekly is a personalized mixtape of 30 highly personalized songs that's curated and delivered to Spotify's 75M active users every Monday. It's received high acclaim in the press and reached 1B streams within its first 10 weeks. In this slide deck we dive into the narrative of how Discover Weekly came to be, highlighting technical challenges, data driven development, and the Machine Learning models used to power our recommendations engine.
Crafting Recommenders: the Shallow and the Deep of it! Sudeep Das, Ph.D.
I present a brief review, and an outlook on the rapid changes happening in the field of recommendation engine research on the heels of the deep learning revolution!
Netflix is the world’s leading Internet television network with over 48 million members in more than 40 countries enjoying more than one billion hours of TV shows and movies per month, including original series. Netflix uses machine learning to deliver a personalized experience to each one of our 48 million users.
In this talk you will hear about the machine learning algorithms that power almost every part of the Netflix experience, including some of our recent work on distributed Neural Networks on AWS GPUs. You will also get an insight into the innovation approach that includes offline experimentation and online AB testing. Finally, you will learn about the system architectures that enable all of this at a Netflix scale.
by Harald Steck (Netflix Inc., US), Roelof van Zwol (Netflix Inc., US) and Chris Johnson (Spotify Inc., US)
Slides of the tutorial on interactive recommender systems at the 2015 conference on Recommender Systems (RecSys).
Interactive recommender systems enable the user to steer the received recommendations in the desired direction through explicit interaction with the system. In the larger ecosystem of recommender systems used on a website, it is positioned between a lean-back recommendation experience and an active search for a specific piece of content. Besides this aspect, we will discuss several parts that are especially important for interactive recommender systems, including the following: design of the user interface and its tight integration with the algorithm in the back-end; computational efficiency of the recommender algorithm; as well as choosing the right balance between exploiting the feedback from the user as to provide relevant recommendations, and enabling the user to explore the catalog and steer the recommendations in the desired direction.
In particular, we will explore the field of interactive video and music recommendations and their application at Netflix and Spotify. We outline some of the user-experiences built, and discuss the approaches followed to tackle the various aspects of interactive recommendations. We present our insights from user studies and A/B tests.
The tutorial targets researchers and practitioners in the field of recommender systems, and will give the participants a unique opportunity to learn about the various aspects of interactive recommender systems in the video and music domain. The tutorial assumes familiarity with the common methods of recommender systems.
DATE: Wednesday, Sept 16, 2015, 11:00-12:30
A Multi-Armed Bandit Framework For Recommendations at NetflixJaya Kawale
In this talk, we present a general multi-armed bandit framework for recommendations on the Netflix homepage. We present two example case studies using MABs at Netflix - a) Artwork Personalization to recommend personalized visuals for each of our members for the different titles and b) Billboard recommendation to recommend the right title to be watched on the Billboard.
So, what will the future UX of TV look like?
How are people watching TV nowadays ?
What kind of Devies We are Using ?
TV or Internet TV ? Smart TV ?
Future for Big Brands
Apple TV UX: 8 Guidelines for greater Apps CELLULAR
"The future of TV is apps."
The new Apple TV raises the bar for TV boxes. Especially the new remote offers a whole new "navigation-feeling" to the user.
But how do you build an outstanding app for the new Apple TV?
We worked ad are working on several apps for Apple TV. From these projects we derived 8 guidelines for greater apps
Icareus Solutions TV Everywhere PresentationJessica Glad
Icareus helps TV broadcasters and channels to reach their TV audience at anytime and anywhere with Icareus TV Everywhere solution.
Icareus TV Everywhere solution includes service Icareus Suite service management platform and TV applications to almost on any device that broadcaster and TV channel can utilize: HbbTVs, SmartTVs, desktop environments, tablets and mobiles. Feature set is configures based on needs and can be e.g. Live TV, Catch-up TV, EPG, Favorites, Social media and multi-language interfaces, just to name the core ones.
By using Icareus TV Everywhere app templates you can achieve a fast-to-market, positively distinctive and competitive deployment. For those who require that “little extra” we offer our professional services to build custom solutions.
Version 20170620
How Forward-Thinking SMBs Think About Document ManagementXerox
How the most forward-thinking SMBs are cutting costs, increasing efficiency and growing their business by optimizing their print environment.
Learn More: https://www.xerox.com/en-us/small-business
This case study was done as a part of my class assignment for Introduction of Analytics. It explains how Netflix uses Big Data and why is so successful.
Why I chose Netflix
Netflix: Stepping into Streaming
CLV used in Netflix
How Netflix uses Big Data and Analytics
Latest Relevant News!!
Conclusion
Netflix, Hybrid Case Study - Netflix and Robert W. Lucas' Skills for SuccessGlenn Burnett
Attached is a sample of my innovative spirit. It's a slide deck from a recent team project I managed for a school assignment. It recaps our teacher's textbook (10 chapters) filtered through the specter of Netflix Customer Service research data. The “Table of Contents” outlines chapters. Alternate slides point to references from 10 articles, paired with key concepts from each chapter.
Netflix, Hybrid Case Study (Slideshow) - Netflix and Robert W. Lucas' Skills ...Glenn Burnett
Attached is a sample of my innovative spirit. It's a slide deck from a recent team project I managed for a school assignment. It recaps our teacher's textbook (10 chapters) filtered through the specter of Netflix Customer Service research data. The “Table of Contents” outlines chapters. Alternate slides point to references from 10 articles, paired with key concepts from each chapter. This slideshow is meant to be paired with the report of the same title.
ContentWise evolved from Recommender Engine to UX Personalization Autopilot: a complete solution to automate the digital storefront and assist the editorial curation process.
A Netflix clone app is essentially a replica of the popular streaming platform. It's a custom-built application designed to mimic the key features and functionalities of Netflix, allowing users to enjoy a wide range of multimedia content through a familiar and user-friendly interface
Creating a Netflix clone app can be a rewarding venture, provided you offer a compelling content library, a user-friendly experience, and strong marketing efforts. While replicating the success of Netflix is no small feat, the demand for streaming services continues to grow, offering ample opportunities for innovation and differentiation in the market.
Every business should be using video to market their goods and services, and it's easier than many business owners think. The key is understanding the right things to do to raise the viewing odds.
Copy of All AbouHow To Create A Streaming App Like Netflix: An Emerging Trend...LorryThomas1
Television and broadcasting channels nowadays feel like a source of good snoozing for the audience. Now, viewers just easily hit the level of boredom when they cannot find their likable source of entertainment on these airing stations. Understanding the psyche of the avid watchers, Netflix was released on everyone’s personal devices and turned about the whole way of watching our handpicked shows.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
4. Today’s objectives
2 1
Share the findings of observing how Netflix uses
personalization as a competitive advantage in engaging
and retaining users and planning content acquisition
Show how you can connect the dots and take advantage
of those findings for your online video services
7. It’s about the pleasure of choice and the abundance of options
It’s understanding needs and narrowing the options
to make choosing a pleasant experience
9. It’s not just high-tech
Brian & Doreen remember customers’ taste, curate and organize shelves,
plan promotions, update the display windows,
listen to requests and recommend products and let customers browse
their shop in Somerset, UK
10.
11. Netflix
Hulu
Amazon
Home page (web)
Pure S-VOD
S-VOD Prime
Free, ad-based VOD
Upsell S-VOD Hulu+
T-VOD
12. So what’s the problem?
Broad range of user types and tastes
Fragmentation of content sources and applications: too many places to look at
Content availability can be very dynamic over time
Most UI solutions rely on drill-down and user efforts
User’s attention span and screen real estate are very limited
You name it!
13. See the opportunity?
Consumption increases
Habits formation occurs
Users feel more engaged
Things can happen when
people have a truly
personalized experience
Your service becomes a destination for unified discovery
People talk about your brand with passion
Subscribers perceive the value and the abundance of what you offer
15. 10 lessons learned from Netflix
The information, interpretations, advice and recommendations contained in this presentation are not
endorsed in any way by Netflix and are based on information publicly shared by Netflix or its employees.
17. 10 lessons learned from Netflix
1. Set objectives and pick metrics
Netflix: maximize member satisfaction and
month-to-month subscription retention
Example Metrics
18. 10 lessons learned from Netflix
1. Set objectives and pick metrics
Example Metrics
Canceled subscriptions per month
Interactive sessions resulting in a playback
Played minutes per user per month
Fully watched playbacks
Interaction time before starting a playback
Returning users
22. 10 lessons learned from Netflix
2. Consider UX as mission-critical
Changes in UI behavior can have a dramatic impact on key metrics.
Multiscreen? Make sure behavior is consistent across devices
Pay special attention to
cross-screen consistency of
Welcome screens
Frequent user actions
User “lost” actions
Leverage the UX Engine
to control UI behavior
across all screens
24. 10 lessons learned from Netflix
3. Personalize UX as much as possible
At Netflix, more than 75% of views
come from some sort of
recommendation or personalized ranking
Views %
from personalized ranking
source: Netflix
0 25 50 75 100
25. 10 lessons learned from Netflix
3. Personalize UX as much as possible
User’s attention span is very limited
The first 8-12 seconds are critical
Weinreich et al. - ACM 2008
Desired outcomes
- find something to watch
- engage in some sort of exploration
26. 10 lessons learned from Netflix
3. Personalize UX as much as possible
Screen real estate is very limited too
Ideally user should
find relevant content
in the first screen
27. 10 lessons learned from Netflix
3. Personalize UX as much as possible
A
Ineffective sort criteria Effective criteria
B
Alphabetic
C
By release year
Personalized order
By ingestion order
D
Canned categories
Even “computed” lists such as
E
Most popular
Most viewed
F
Recently added
and My list!
28. 10 lessons learned from Netflix
3. Personalize UX as much as possible
There are two “folds”
Netflix personalizes in both directions
1. ranking of items in a carousel
2. ranking of carousels in the layout
2
Real estate
“above-the-fold”
1
29. 10 lessons learned from Netflix
3. Personalize UX as much as possible
Featured content
Resume play + My list
{
Most likely actions
Popular
Top picks for you
Recently added
Main genres
Pseudo-genres
Because you watched…
Friends watching…
Watch again
Displayed in a
personalized order"
"
Some of them disappear for
a while if never “touched” "
or because of A/B Testing
30. 10 lessons learned from Netflix
3. Personalize UX as much as possible
Personalized reordering may be
disorienting for some people
Indeed some Netflix users
complain about this;
but it seems to be effective
and we’ll show you
how to handle it
32. 10 lessons learned from Netflix
4. Understand user’s lifestyle and context
33. 10 lessons learned from Netflix
4. Understand user’s lifestyle and context
Netflix mines usage data to extract behavior patterns
Personalization may be affected by
context elements
Device type
Time of the day
Day of the week
Season of the year
User is at-home or out-of-home
Geo-location (traveling, commuting, weekend-home…)
Local weather
Popular news
Other users in close proximity (phones/wearables)
34. 10 lessons learned from Netflix
5. Use interaction data then ask for feedback
Priority on high-value usage events
Playback start/stop/resume
View asset details
Add to personal list
Other interactions
Trick-play control
Search
Sharing
Navigation paths…
Ask for feedback
5-stars
Like
Dislike
Love it!
35. 10 lessons learned from Netflix
6. Let users know how the service is adapting
to their tastes
36. 10 lessons learned from Netflix
6. Let users know how the service is adapting
to their tastes
Promote trust in the system
Encourage users to give feedback
Better personalization
37. 10 lessons learned from Netflix
6. Let users know how the service is adapting
to their tastes
Use meaningful labels
referring to past behavior
user can recognize
Because you watched Breaking Bad
Because of your interest for Time Travel
Because you loved Kill Bill Vol.1
38. 10 lessons learned from Netflix
7. Ensure metadata captures content nuances
and is consistent
39. 10 lessons learned from Netflix
7. Ensure metadata captures content nuances
and is consistent
Actors, Directors, Writers
Genres
Synopsis Release Year
Duration
Country Studio
Language
Characters Topics Themes Moods
Locations Time Periods
Keywords - Microtags
“NETFLIX QUANTUM THEORY”
A set of best practices for manual
micro-tagging of video content
Social acceptability of the lead character
40. 10 lessons learned from Netflix
7. Ensure metadata captures content nuances
and is consistent
Let users search for content you don’t have
41. 10 lessons learned from Netflix
7. Ensure metadata captures content nuances
and is consistent
With richer content metadata
you can use analytics to
understand content performance
and drive content acquisition
(or even original production)
And add meaning to user profiles
44. 10 lessons learned from Netflix
8. Give reasons to come back often
Refresh catalog frequently - OR - Let the UX Engine do it for you (virtually)
45. 10 lessons learned from Netflix
8. Give reasons to come back often
Re-shuffle top items to periodically
change the ones above-the-fold
Items outside the first screen are
still highly relevant for the user
User perceives novelty and will
be keen to return more often
46. 10 lessons learned from Netflix
9. Run frequent UI experiments
There is no “perfect way” and there are many types of users:
experiments and adaptation seem to be the most effective ways
Identify the UI elements on the path to the key goals
Roll-out the variations and look at 2-5 metrics
Run the experiments for two weeks or until statistical validity
Design and plan experiments not to interfere with each other
Experiments consume interaction events:
make sure there is enough activity to feed all of the active variations
47. 10 lessons learned from Netflix
10. Close the loop, base decisions upon data
48. 10. Close the loop, base decisions upon data
Netflix was the only network that
said “We believe in you. We’ve run
our data, and it tells us that our
audience would watch this series.
We don’t need you to do a pilot”
Kevin Spacey, actor and producer
Listen to Kevin saying this (video)
49. 10 lessons learned from Netflix
10. Close the loop, base decisions upon data
Netflix uses analytics to heavily influence the
content acquisition policy
Netflix proved to be agile and effective in rolling out variations and
track several metrics across hundreds of client platforms
Netflix team is very disciplined on reporting UI events.
This enables full visibility in analytics and higher ROI
Yes. At Netflix they go nuts for analytics!
And they look to be right
50. 10 Lessons from Netflix - Recap
1. Set objectives, pick metrics and share them with the team
2. Consider UX as mission-critical
3. Personalize UX as much as possible
4. Understand user’s lifestyle and context
5. Use interaction data then ask for feedback
6. Let users know your service is adapting to their tastes
7. Ensure metadata captures content nuances and is consistent
8. Give reasons to come back often
9. Run frequent UI experiments
10. Close the loop and base your decisions upon data
51. Netflix solutions are applicable (and applied) at… Netflix
Other services may include S-VOD
as well as Linear TV, DVR,
Transactional VOD, Pay TV, Pay-per view,
music videos, sports highlights,
Advertising or User-generated Content…
We need a way to turn these lessons into practice
touching all the stakeholders in our projects
52. "
UIDO
A set of checklists to guide
you while introducing
personalization in your
video service
53. What you deliver How you start
User Experience Integrator Experience
UX IX
DX OX
Developer Experience Operator Experience
How you build it Tools to manage "
UIDO
55. UX User Experience What you deliver
Content types ✓ Movies
Aggregates ✓ Collections
✓ Series
✓ Episodes
✓ Extras
✓ Music videos
✓ Playlists
✓ News
✓ Sports events
✓ Sports highlights
✓ Scheduled programs
✓ Channels
✓ _____________________
✓ Seasons
✓ Channel bundles
✓ Movie bundles
✓ Sports Team bundles
✓ Sports League bundles
✓ __________________
56. UX User Experience What you deliver
Key UX features ✓ Manually curated collections
✓ Search results
✓ Search suggestions while you type (single/multi-type)
✓ Search refine with smart filters (facets)
✓ Similar content
✓ Personalized picks for user
✓ Critics-based feed (Rotten Tomatoes, Metacritic…)
✓ Series you watch (with next-episode)
✓ VOD bookmarking (resume playback)
✓ User’s list
✓ Predictive browsing (surfacing folders)
✓ Personalized pseudo-genres
57. Reference UI
"
Showing most of the
personalization use cases
supported by ContentWise
58.
59. UX User Experience What you deliver
Key UX features
(cont’d)
✓ Social graph (e.g. friends, followers)
✓ Sharing actions
✓ Content can be embedded
✓ Co-watching (blended profiles)
✓ Profile explanation with content metadata
✓ User can rate content (stars, like, dislike, love, etc.)
60. UX User Experience What you deliver
For kids ✓ Parental ratings
✓ Kids mode
✓ Specialized metadata (e.g. Commonsense)
✓ Editorial curation
✓ Curation by parents
✓ Analytics for parents
61. UX User Experience What you deliver
Content sources ✓ Linear schedule (line-ups)
✓ Start-over TV system
✓ VOD Catalog
✓ Local DVR
✓ Network DVR
✓ Reverse EPG (catch-up)
✓ ______________
62. UX User Experience What you deliver
Device types ✓ Phone
✓ Tablet
✓ PC
✓ TV
✓ Watch
Access models ✓ S-VOD
✓ T-VOD
✓ Ad-VOD
✓ Free-Linear
✓ Pay-Linear
✓ PPV
Profile types ✓ Personal
✓ Household
✓ Main account powers
✓ Blended
✓ Personas templates
✓ Personal on device
Access locations ✓ At-home, OOH
✓ On-net, off-net
63. UX User Experience What you deliver
Entitlements ✓ S-VOD packages
✓ Rented movies
✓ Purchased movies
✓ Purchased seasons
✓ Purchased episodes
✓ Subscribed channels
✓ Subscribed bundles (e.g. Channel + S-VOD)
✓ ______________________
64. Explaining a recommendation
Because you liked
these other movies
Affinity between the
user’s taste and the
recommended movie
(using the tag structure)
ContentWise Reference UI
66. OX Operator Experience How to manage
✓ Managing UI Elements with UX Engine
✓ Creating and updating editorial lists
✓ Generating and curating pseudo-genres
✓ Accessing analytics
✓ Content planning using analytics
✓ Managing variations and experiments for A/B Testing
✓ Understanding the impact of business rules on key metrics
67.
68. Personalized pseudo-genres
INTENSE ACTION MOVIES
mood genre type
2000s AUSTRALIAN THRILLER MOVIES
release prod
genre type
year
country
AMERICAN DRAMA MOVIES STARRING TOM HANKS
ContentWise Reference UI
69. The magic of richer metadata
MOVIES FROM FEMALE DIRECTORS
type person role
MOVIES STARRING A ROCKSTAR
type
gender
from semantic
enrichment
looking
into actors
person role
from semantic
enrichment
ContentWise Reference UI
70. Curation of Pseudo-genres Metadata fields
considered for
labels
Status of the
pseudo-genre
Type:
Editorial
or
Computed
ContentWise Management Console
71. Driving from the UX Engine
Rendered by UI code
Configured by UX Engine
ContentWise Management Console
73. Content planning - Choosing items to retire
Find movies with a small
number of “estimated”
residual views
and are “expiring”
Automatically create a
business rule
The rule can be used in A/B Testing
to anticipate the impact of removing
these movies from the catalog.
ContentWise Management Console
74. A/B/C Testing
Biz rule #1
Biz rule #N
Variation A
Biz rule #1
Biz rule #N
Variation B
Experiment
Group A
Group B
Control Group
Results Metrics
Normal
behavior
81. IX Integrator Experience How to start
✓ Content model map
✓ Event model map
✓ User ID map
✓ Data refresh policy
✓ Bulk ingestion automation
✓ Delta updates automation
✓ Client applications map
✓ UI elements to be managed from UX Engine
82. Thank you!
For more information, please visit our website or contact us
pancrazio.kauser.kanji@vodprofessional.com auteri@contentwise.tv
Digital TV. Personalized
www.vodprofessional.com www.contentwise.tv