presentation on recommendation algorithms given by Hossein Taghavi (Netflix) at RecSys2016 workshop on TV in Boston.
Thsi concerns the Continue Watching algortihms used by Netflix to recommend content to users
Our objective for the Netflix recommendation engine is to create a personalized experience for our members, making it easier for them to find a video to watch and enjoy. When a member logs on to the service, she/he may be in one or a combination of different watching modes: discovering a new content to watch, continuing to watch a partially-watched movie or a TV show she/he has been binging on, playing one of the contents she/he had put in her play list during an earlier session, etc. If, for example, we can reasonably predict when a member is more likely to be in the continuation mode, and which videos she/he is more likely to resume, it makes sense to place those videos in more prominent places of the home page. In this talk we focus on understanding the discovery vs. continuation behavior and explain how we have used machine learning to improve the member experience by learning a personalized balance between those two modes. As a case study, we focus on a recent change on the personalization of a row of recommendations called “Continue Watching,” which appears on the main page of the Netflix member homepage on the website and the app and currently drives a significant proportion of member streaming hours.
Netflix Recommendations Using Spark + Cassandra (Prasanna Padmanabhan & Roopa...DataStax
Learning is an analytic process of exploring the past in order to predict the future. Hence, being able to travel back in time to create features is critical for machine learning projects to be successful. To enable this, we built a time machine that computes features for any arbitrary time in the recent past for offline experimentation. We also built a real-time stream processing system to capture the interests of members during different times of the day and to quickly adapt to changes in the collective interests of members as it happens in case of real-world events.
Building the time machine for offline experimentation and the real-time infrastructure for online recommendations with Apache Spark (Streaming) and Apache Cassandra empowered us to both scale up the data size by an order of magnitude and train and validate the models in less time. We will delve into the architecture, use case details, data models used for cassandra and share our learnings.
About the Speakers
Prasanna Padmanabhan Engineering Manager, Netflix
Prasanna leads the Data Systems for Personalization team at Netflix. His primary focus is on building various big data infrastructure components that help their algorithmic engineers to innovate faster and improve personalization for Netflix members. In the past, he has built distributed data systems that leverages both batch and stream processing.
Roopa Tangirala Engineering Manager, Netflix
Roopa Tangirala is an experienced engineering leader with extensive background in databases, be they distributed or relational. She manages the database engineering team at Netflix responsible for operating cloud persistent and semipersistent runtime stores for Netflix, which includes Cassandra, Elasticsearch, Dynomite and MySQL databases, by ensuring data availability, durability, and scalability to meet the growing business needs.
Wrap Up EBU Big Data and Society conference at RTBF - Day 2 (13 december 2016)IntoTheMinds
This is the wrap up of the first day of the EBU Big Data and Society conference that was held at RTBF on 12 and 13 december 2016.
This presentations sumps up the takeaways of the presentation by Jean-Paul Philippot, Prof. Wehenkel, Steven Bourke, Prof. Malthouse, PN Schwab, Evan Estola
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.
Our objective for the Netflix recommendation engine is to create a personalized experience for our members, making it easier for them to find a video to watch and enjoy. When a member logs on to the service, she/he may be in one or a combination of different watching modes: discovering a new content to watch, continuing to watch a partially-watched movie or a TV show she/he has been binging on, playing one of the contents she/he had put in her play list during an earlier session, etc. If, for example, we can reasonably predict when a member is more likely to be in the continuation mode, and which videos she/he is more likely to resume, it makes sense to place those videos in more prominent places of the home page. In this talk we focus on understanding the discovery vs. continuation behavior and explain how we have used machine learning to improve the member experience by learning a personalized balance between those two modes. As a case study, we focus on a recent change on the personalization of a row of recommendations called “Continue Watching,” which appears on the main page of the Netflix member homepage on the website and the app and currently drives a significant proportion of member streaming hours.
Netflix Recommendations Using Spark + Cassandra (Prasanna Padmanabhan & Roopa...DataStax
Learning is an analytic process of exploring the past in order to predict the future. Hence, being able to travel back in time to create features is critical for machine learning projects to be successful. To enable this, we built a time machine that computes features for any arbitrary time in the recent past for offline experimentation. We also built a real-time stream processing system to capture the interests of members during different times of the day and to quickly adapt to changes in the collective interests of members as it happens in case of real-world events.
Building the time machine for offline experimentation and the real-time infrastructure for online recommendations with Apache Spark (Streaming) and Apache Cassandra empowered us to both scale up the data size by an order of magnitude and train and validate the models in less time. We will delve into the architecture, use case details, data models used for cassandra and share our learnings.
About the Speakers
Prasanna Padmanabhan Engineering Manager, Netflix
Prasanna leads the Data Systems for Personalization team at Netflix. His primary focus is on building various big data infrastructure components that help their algorithmic engineers to innovate faster and improve personalization for Netflix members. In the past, he has built distributed data systems that leverages both batch and stream processing.
Roopa Tangirala Engineering Manager, Netflix
Roopa Tangirala is an experienced engineering leader with extensive background in databases, be they distributed or relational. She manages the database engineering team at Netflix responsible for operating cloud persistent and semipersistent runtime stores for Netflix, which includes Cassandra, Elasticsearch, Dynomite and MySQL databases, by ensuring data availability, durability, and scalability to meet the growing business needs.
Wrap Up EBU Big Data and Society conference at RTBF - Day 2 (13 december 2016)IntoTheMinds
This is the wrap up of the first day of the EBU Big Data and Society conference that was held at RTBF on 12 and 13 december 2016.
This presentations sumps up the takeaways of the presentation by Jean-Paul Philippot, Prof. Wehenkel, Steven Bourke, Prof. Malthouse, PN Schwab, Evan Estola
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.
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.
Big & Personal: the data and the models behind Netflix recommendations by Xa...BigMine
Since the Netflix $1 million Prize, announced in 2006, our company has been known for having personalization at the core of our product. Even at that point in time, the dataset that we released was considered “large”, and we stirred innovation in the (Big) Data Mining research field. Our current product offering is now focused around instant video streaming, and our data is now many orders of magnitude larger. Not only do we have many more users in many more countries, but we also receive many more streams of data. Besides the ratings, we now also use information such as what our members play, browse, or search.
In this talk I will discuss the different approaches we follow to deal with these large streams of data in order to extract information for personalizing our service. I will describe some of the machine learning models used, as well as the architectures that allow us to combine complex offline batch processes with real-time data streams.
Personalization - 10 Lessons Learned from NetflixPancrazio Auteri
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.
Netflix JavaScript Talks - Scaling A/B Testing on Netflix.com with Node.jsChris Saint-Amant
At Netflix we run hundreds of A/B tests every year. Maintaining multivariate experiences quickly adds strain to any UI engineering team. In this talk, Alex Liu and Micah Ransdell explore the patterns we’ve built in Node.js to tame this beast - ultimately enabling quick feature development and rapid test iteration on our service used by over 50 million people around the world.
Patterns of the Lambda Architecture -- 2015 April - Hadoop Summit, EuropeFlip Kromer
This talk centers on two things: a set of patterns for the architecture of high-scale data systems; and a framework for understanding the tradeoffs we make in designing them.
Keys To World-Class Retail Web Performance - Expert tips for holiday web read...SOASTA
As Walmart.com’s former head of Performance and Reliability, Cliff Crocker knows large scale web performance. Now SOASTA’s VP of products, Cliff is pouring his passion and expertise into cloud testing to solve the biggest challenges in mobile and web performance.
The holiday rush of mobile and web traffic to your web site has the potential for unprecedented success or spectacular public failure. The world’s leading retailers have turned to the cloud to assure that no matter what load, mobile and web apps will delight customers and protect revenue.
Join us as Cliff explores the key criteria for holiday web performance readiness:
Closing the gap in front- and back-end web performance and reliability
Collecting real user data to define the most realistic test scenarios
Preparing properly for the virtual walls of traffic during peak events
Leveraging CloudTest technology, as have 6 of 10 leading retailers
[DSC Europe 23] Vladislav Belov - ChatBot Learning Assistant with Large Langu...DataScienceConferenc1
Join this talk as I reveal the fascinating journey of creating a cutting-edge bot assistant powered by Large Language Models, paving the way from POC to MVP. Prepare to dive deeper into: A unique multi-route architecture that forms the backbone of our bot's performance., Ingenious solutions to combat latency challenges for a seamless user experience., Techniques to tackle out-of-distribution questions and enhance the bot's adaptability. Don't miss the opportunity to discover valuable insights that could elevate your own bot development process. See you there!
Video Recommendation Engines as a ServiceKamil Sindi
JW Player is the world’s largest network-independent video platform representing 5 percent of global internet video. One of the core services it offers video publishers are turn-key recommendations that can drive higher engagement among their viewers. This talk will focus on the challenges of building and improving recommendations algorithms at JW Player's scale.
Majestic Workshop on Backlinks and Link BuildingSante J. Achille
My Workshop as Majestic Brand Ambassador at SMXL Milan 2019 on links and link building strategies: "Redefining Backlinks and Link Building Strategies".
Advanced Testing and Debugging using the Developer Console webinarSalesforce Developers
The Developer Console is your one-stop shop for developing on the Force.com platform. This application allows you to author and debug Apex code, create Visualforce pages, run tests on your programmatic artifacts, and to inspect your org data via query.
Join us as we dig deeper into the comprehensive debugging, profiling, and testing capabilities of the Developer Console.
Watch this webinar to:
Learn how to run tests using the Developer Console
See how to examine log results using the Developer Console
Understand how to analyze the performance of your org’s operations
Learn about checkpoints, the Force.com equivalent of breakpoints, for debugging
Explore the query editor functionality to get better access to your data
Adina Levin and Tracy Ruggles from Socialtext share their experience with Agile Product Management Methodologies with Social Software. A video of this presentation is available here http://www.viddler.com/explore/socialtext/videos/4/
Sorry the font is messed up.
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.
Big & Personal: the data and the models behind Netflix recommendations by Xa...BigMine
Since the Netflix $1 million Prize, announced in 2006, our company has been known for having personalization at the core of our product. Even at that point in time, the dataset that we released was considered “large”, and we stirred innovation in the (Big) Data Mining research field. Our current product offering is now focused around instant video streaming, and our data is now many orders of magnitude larger. Not only do we have many more users in many more countries, but we also receive many more streams of data. Besides the ratings, we now also use information such as what our members play, browse, or search.
In this talk I will discuss the different approaches we follow to deal with these large streams of data in order to extract information for personalizing our service. I will describe some of the machine learning models used, as well as the architectures that allow us to combine complex offline batch processes with real-time data streams.
Personalization - 10 Lessons Learned from NetflixPancrazio Auteri
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.
Netflix JavaScript Talks - Scaling A/B Testing on Netflix.com with Node.jsChris Saint-Amant
At Netflix we run hundreds of A/B tests every year. Maintaining multivariate experiences quickly adds strain to any UI engineering team. In this talk, Alex Liu and Micah Ransdell explore the patterns we’ve built in Node.js to tame this beast - ultimately enabling quick feature development and rapid test iteration on our service used by over 50 million people around the world.
Patterns of the Lambda Architecture -- 2015 April - Hadoop Summit, EuropeFlip Kromer
This talk centers on two things: a set of patterns for the architecture of high-scale data systems; and a framework for understanding the tradeoffs we make in designing them.
Keys To World-Class Retail Web Performance - Expert tips for holiday web read...SOASTA
As Walmart.com’s former head of Performance and Reliability, Cliff Crocker knows large scale web performance. Now SOASTA’s VP of products, Cliff is pouring his passion and expertise into cloud testing to solve the biggest challenges in mobile and web performance.
The holiday rush of mobile and web traffic to your web site has the potential for unprecedented success or spectacular public failure. The world’s leading retailers have turned to the cloud to assure that no matter what load, mobile and web apps will delight customers and protect revenue.
Join us as Cliff explores the key criteria for holiday web performance readiness:
Closing the gap in front- and back-end web performance and reliability
Collecting real user data to define the most realistic test scenarios
Preparing properly for the virtual walls of traffic during peak events
Leveraging CloudTest technology, as have 6 of 10 leading retailers
[DSC Europe 23] Vladislav Belov - ChatBot Learning Assistant with Large Langu...DataScienceConferenc1
Join this talk as I reveal the fascinating journey of creating a cutting-edge bot assistant powered by Large Language Models, paving the way from POC to MVP. Prepare to dive deeper into: A unique multi-route architecture that forms the backbone of our bot's performance., Ingenious solutions to combat latency challenges for a seamless user experience., Techniques to tackle out-of-distribution questions and enhance the bot's adaptability. Don't miss the opportunity to discover valuable insights that could elevate your own bot development process. See you there!
Video Recommendation Engines as a ServiceKamil Sindi
JW Player is the world’s largest network-independent video platform representing 5 percent of global internet video. One of the core services it offers video publishers are turn-key recommendations that can drive higher engagement among their viewers. This talk will focus on the challenges of building and improving recommendations algorithms at JW Player's scale.
Majestic Workshop on Backlinks and Link BuildingSante J. Achille
My Workshop as Majestic Brand Ambassador at SMXL Milan 2019 on links and link building strategies: "Redefining Backlinks and Link Building Strategies".
Advanced Testing and Debugging using the Developer Console webinarSalesforce Developers
The Developer Console is your one-stop shop for developing on the Force.com platform. This application allows you to author and debug Apex code, create Visualforce pages, run tests on your programmatic artifacts, and to inspect your org data via query.
Join us as we dig deeper into the comprehensive debugging, profiling, and testing capabilities of the Developer Console.
Watch this webinar to:
Learn how to run tests using the Developer Console
See how to examine log results using the Developer Console
Understand how to analyze the performance of your org’s operations
Learn about checkpoints, the Force.com equivalent of breakpoints, for debugging
Explore the query editor functionality to get better access to your data
Adina Levin and Tracy Ruggles from Socialtext share their experience with Agile Product Management Methodologies with Social Software. A video of this presentation is available here http://www.viddler.com/explore/socialtext/videos/4/
Sorry the font is messed up.
Code Palousa presentation- "Giving Digital Eyes to your Synthetic Tests"Christopher Hamm
My project combines open source technologies of Tensorflow with major computer vision model to create a powerful computer vision API. In the project, it can evaluate confidence levels for each labels using good training data. The practical application example will include the computer vision API integrated with a Selenium test script setup. The end result is a robust visual testing tool that can determine if a page compares better to a working state vs a failing state.
Pega Lead System Architecture (CPLSA) Exam | Start Your PreparationMeghna Arora
Click Here---> https://bit.ly/3C1zZZD <---Get complete detail on CPLSA exam guide to crack CPLSA version 8.8. You can collect all information on CPLSA tutorial, practice test, books, study material, exam questions, and syllabus. Firm your knowledge on CPLSA version 8.8 and get ready to crack CPLSA certification. Explore all information on CPLSA exam with number of questions, passing percentage and time duration to complete test.
At this point, you may be familiar with the design of MongoDB databases and collections – but what are the frequent patterns you may have to model?
This presentation will add knowledge of how to represent common relationships (1-1, 1-N, N-N) in MongoDB. Going further than relationships, this presentation identifies a set of common patterns, in a similar way to what the Gang of Four did for Object Oriented Design. Finally, this presentation will guide you through the steps of modeling those patterns in MongoDB collections.
In this session, you will learn about:
How to create the appropriate MongoDB collections for some of the patterns discussed.
Differences in relationships vs. the relational database world, and how those differences translate to MongoDB collections.
Common patterns in developing applications with MongoDB, plus a specific vocabulary with which to refer to them.
At this point, you may be familiar with the design of MongoDB databases and collections – but what are the frequent patterns you may have to model?
This presentation will add knowledge of how to represent common relationships (1-1, 1-N, N-N) in MongoDB. Going further than relationships, this presentation identifies a set of common patterns, in a similar way to what the Gang of Four did for Object Oriented Design. Finally, this presentation will guide you through the steps of modeling those patterns in MongoDB collections.
In this session, you will learn about:
How to create the appropriate MongoDB collections for some of the patterns discussed.
Differences in relationships vs. the relational database world, and how those differences translate to MongoDB collections.
Common patterns in developing applications with MongoDB, plus a specific vocabulary with which to refer to them.
Présentation créée pour une publication Linkedin du 25/05/20.
Élaboration d'un scénario de reprise économique après la crise du Covid.
Nous développons en particulier l'importance de la confiance des consommateurs pour la reprise et expliquons pourquoi il faudra attendre Janvier 2021 pour voir une lente remontée.
The advertising campaigns run in Belgium during the Covid-19 crisisIntoTheMinds
This presentation gives an overview of all advertising campaigns that were run in Belgium during the coronavirus crisis.
It helps understand how brands changed their message to cope with the situation.
Presentation Stéphane Saulnier at EBU Conference "data in the newsroom"IntoTheMinds
Stéphane Saulnier of FigData (data journalism department of Le Figaro, France) presented the various projects carried out in the field of data visualizations.
Purchase drivers for iconic products in the luxury sectorIntoTheMinds
research was carried out by a team of 4 researchers from INSEEC business school (see reference at the end of this article) and was entitled “Timeless luxury: what drives the purchase of iconic products“.
Presentation given by MAALEJ M., BENZI M., BEGUET M., SALVADOR M. of INSEEC Business School, France
Credits : courtesy authors
Presentation given by Jarkko Ryynänen and Aki Kekäläinen at EBU conference on artificial intelligence in the broadcasting industry on 8-9 November 2018 in Geneva.
Toon borré presentation at Meetup Big Data and Ethics at DigitYser Brussels 1...IntoTheMinds
presentation given at the first meetup on Big Data and Ethics given at DigitYser Brussels. Find more about this event on our blog at www.intotheminds.com/blog/en
Leenke De Donder presentation at Meetup Big Data and Ethics at DigitYser Brus...IntoTheMinds
presentation given at the first meetup on Big Data and Ethics given at DigitYser Brussels. Find more about this event on our blog at www.intotheminds.com/blog/en
Jochanen eynikel presentation at Meetup Big Data and Ethics at DigitYser Brus...IntoTheMinds
presentation given at the first meetup on Big Data and Ethics given at DigitYser Brussels. Find more about this event on our blog at www.intotheminds.com/blog/en
Thomas carette presentation at Meetup Big Data and Ethics at DigitYser Brusse...IntoTheMinds
presentation given at the first meetup on Big Data and Ethics given at DigitYser Brussels. Find more about this event on our blog at www.intotheminds.com/blog/en
Big Data and ethics meetup : slides presentation michael ekstrandIntoTheMinds
Those are the slides of the speech given by Prof. Michael Ekstrand at the Meetup on Big Data and Ethics at DigitYser (Brussels) on 15 June 2017. For more info visit http://www.intotheminds.com/blog/en/big-data-and-ethics-first-sucessful-meetup-at-digityser-in-brussels/
Presentatie big data (Dag van de verkoper, Cevora) IntoTheMinds
Presentatie gegeven in Antwerpen en Gent of 30 Mei 2017 en 18 Mei 2017 over Big Data en verkoop.
In deze introductie werd de theorie over Big Data uitgelegd zoals voorbeelden van toepassingen om data te valoriseren. Speciaal aandacht werd gevestigd op juridische aspecten zoals GDPR.
Slides pierre nicolas schwab DISummit 2017 (Big Data, Brussels)IntoTheMinds
In this presentation Pierre-Nicolas Schwab, Head of Big Data at RTBF, deals with the design of ethical algorithms and the steps undertaken at RTBF to have a GDPR-compliant Big Data strategy.
"Building Trust" discussion panel at EBU Big Data conference 2017 (Pierre-Nic...IntoTheMinds
This presentation was given by Pierre-Nicolas Schwab, Head of Big Data at RTBF, on the occasion of the 2nd annual EBU Big Data conference in Geneva (Switzerland)
Unveiling the Secrets How Does Generative AI Work.pdfSam H
At its core, generative artificial intelligence relies on the concept of generative models, which serve as engines that churn out entirely new data resembling their training data. It is like a sculptor who has studied so many forms found in nature and then uses this knowledge to create sculptures from his imagination that have never been seen before anywhere else. If taken to cyberspace, gans work almost the same way.
Improving profitability for small businessBen Wann
In this comprehensive presentation, we will explore strategies and practical tips for enhancing profitability in small businesses. Tailored to meet the unique challenges faced by small enterprises, this session covers various aspects that directly impact the bottom line. Attendees will learn how to optimize operational efficiency, manage expenses, and increase revenue through innovative marketing and customer engagement techniques.
RMD24 | Debunking the non-endemic revenue myth Marvin Vacquier Droop | First ...BBPMedia1
Marvin neemt je in deze presentatie mee in de voordelen van non-endemic advertising op retail media netwerken. Hij brengt ook de uitdagingen in beeld die de markt op dit moment heeft op het gebied van retail media voor niet-leveranciers.
Retail media wordt gezien als het nieuwe advertising-medium en ook mediabureaus richten massaal retail media-afdelingen op. Merken die niet in de betreffende winkel liggen staan ook nog niet in de rij om op de retail media netwerken te adverteren. Marvin belicht de uitdagingen die er zijn om echt aansluiting te vinden op die markt van non-endemic advertising.
India Orthopedic Devices Market: Unlocking Growth Secrets, Trends and Develop...Kumar Satyam
According to TechSci Research report, “India Orthopedic Devices Market -Industry Size, Share, Trends, Competition Forecast & Opportunities, 2030”, the India Orthopedic Devices Market stood at USD 1,280.54 Million in 2024 and is anticipated to grow with a CAGR of 7.84% in the forecast period, 2026-2030F. The India Orthopedic Devices Market is being driven by several factors. The most prominent ones include an increase in the elderly population, who are more prone to orthopedic conditions such as osteoporosis and arthritis. Moreover, the rise in sports injuries and road accidents are also contributing to the demand for orthopedic devices. Advances in technology and the introduction of innovative implants and prosthetics have further propelled the market growth. Additionally, government initiatives aimed at improving healthcare infrastructure and the increasing prevalence of lifestyle diseases have led to an upward trend in orthopedic surgeries, thereby fueling the market demand for these devices.
Discover the innovative and creative projects that highlight my journey throu...dylandmeas
Discover the innovative and creative projects that highlight my journey through Full Sail University. Below, you’ll find a collection of my work showcasing my skills and expertise in digital marketing, event planning, and media production.
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Business Valuation Principles for EntrepreneursBen Wann
This insightful presentation is designed to equip entrepreneurs with the essential knowledge and tools needed to accurately value their businesses. Understanding business valuation is crucial for making informed decisions, whether you're seeking investment, planning to sell, or simply want to gauge your company's worth.
Skye Residences | Extended Stay Residences Near Toronto Airportmarketingjdass
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Falcon stands out as a top-tier P2P Invoice Discounting platform in India, bridging esteemed blue-chip companies and eager investors. Our goal is to transform the investment landscape in India by establishing a comprehensive destination for borrowers and investors with diverse profiles and needs, all while minimizing risk. What sets Falcon apart is the elimination of intermediaries such as commercial banks and depository institutions, allowing investors to enjoy higher yields.
Memorandum Of Association Constitution of Company.pptseri bangash
www.seribangash.com
A Memorandum of Association (MOA) is a legal document that outlines the fundamental principles and objectives upon which a company operates. It serves as the company's charter or constitution and defines the scope of its activities. Here's a detailed note on the MOA:
Contents of Memorandum of Association:
Name Clause: This clause states the name of the company, which should end with words like "Limited" or "Ltd." for a public limited company and "Private Limited" or "Pvt. Ltd." for a private limited company.
https://seribangash.com/article-of-association-is-legal-doc-of-company/
Registered Office Clause: It specifies the location where the company's registered office is situated. This office is where all official communications and notices are sent.
Objective Clause: This clause delineates the main objectives for which the company is formed. It's important to define these objectives clearly, as the company cannot undertake activities beyond those mentioned in this clause.
www.seribangash.com
Liability Clause: It outlines the extent of liability of the company's members. In the case of companies limited by shares, the liability of members is limited to the amount unpaid on their shares. For companies limited by guarantee, members' liability is limited to the amount they undertake to contribute if the company is wound up.
https://seribangash.com/promotors-is-person-conceived-formation-company/
Capital Clause: This clause specifies the authorized capital of the company, i.e., the maximum amount of share capital the company is authorized to issue. It also mentions the division of this capital into shares and their respective nominal value.
Association Clause: It simply states that the subscribers wish to form a company and agree to become members of it, in accordance with the terms of the MOA.
Importance of Memorandum of Association:
Legal Requirement: The MOA is a legal requirement for the formation of a company. It must be filed with the Registrar of Companies during the incorporation process.
Constitutional Document: It serves as the company's constitutional document, defining its scope, powers, and limitations.
Protection of Members: It protects the interests of the company's members by clearly defining the objectives and limiting their liability.
External Communication: It provides clarity to external parties, such as investors, creditors, and regulatory authorities, regarding the company's objectives and powers.
https://seribangash.com/difference-public-and-private-company-law/
Binding Authority: The company and its members are bound by the provisions of the MOA. Any action taken beyond its scope may be considered ultra vires (beyond the powers) of the company and therefore void.
Amendment of MOA:
While the MOA lays down the company's fundamental principles, it is not entirely immutable. It can be amended, but only under specific circumstances and in compliance with legal procedures. Amendments typically require shareholder
Affordable Stationery Printing Services in Jaipur | Navpack n PrintNavpack & Print
Looking for professional printing services in Jaipur? Navpack n Print offers high-quality and affordable stationery printing for all your business needs. Stand out with custom stationery designs and fast turnaround times. Contact us today for a quote!
4. Netflix Scale
§ > 83M members
§ > 190 countries
§ > 1000 device types
§ > 3.7B hours of content
streamed every month
§ 36% of peak US
downstream traffic
4
5. § Recommendations through
predicted star rating
§ Contest:
§ Accuracy measured by root
mean squared error (RMSE)
§ Improve by 10% = $1 million!
§ Data size:
§ 100M ratings (back then
“almost massive”)
5
6. Turn on Netflix, and the
absolute best contents for you
would automatically start playing
Recommendation System: Ideal State
6
7. Create a page of recommendations
where the titles you are
most likely to watch and enjoy are
shown on the most visible parts of
the page
Meanwhile…
7
8. Title Ranking
Everything is a RecommendationRowSelection&Ordering
Recommendations are
driven by machine
learning algorithms
Over 80% of what
members watch comes
from our
recommendations
8
9. How the Homepage is Built
§ The titles are organized as rows
§ Ordering of titles within rows depends on the row type
§ Selection and ordering of rows:
§ Personalized page generation
algorithm
§ Also some business rules and
constraints
§ Balance thematic coherence,
relevance, and diversity
9
10. Various Types of Member Interactions/Feedback
§ Plays
§ How long, pause, rewind, skip, etc.
§ Rating and social
§ Rate, like, share
§ Context
§ Time, location, device, language
§ Interactions
§ Scrolling, opening a title page,
search, list add 10
11. Building the Recommendations is Data Driven
§ Try an idea offline using historical
data to see if it would have made
better recommendations
§ Offline metrics: AUC, nDCG, Recall, …
§ If it did, deploy a live A/B test to see
if it performs well in Production
§ Primary metric: Member retention
Idea /
Problem
Data
Algorithm
Model
Metrics
A/B
Testing
11
12. For More Reading
§ Netflix tech blog:
§ bit.ly/beyondfivestars
§ bit.ly/learnapage
§ bit.ly/sparktimetravel
12
14. The same you watched last time!
What Is the Most Likely Title You Will Watch?
§ A large portion of watching hours are spent in continue
watching mode
14
15. Different Modes of Watching
§ Continuation: Resume a
recently-watched TV/Movie
§ List: Play a title previously
added to My List
§ Rewatch: Rewatch a title
enjoyed in the past
§ Discovery: Discover a new
title to watch
15
16. Recommending for Different Modes:
Approach 1
§ Build one unified model for ranking the titles in each row
and one for ranking rows
§ Optimized for the likelihood of play/enjoyment from the page
§ Benefits:
§ Fewer models to maintain
§ Fewer A/B tests
16
17. Approach 1: Challenges
§ Members behave differently in different modes
§ Different row types are designed for different behaviors
§ Hard to capture and balance all that in one objective
§ E.g. simply ranking titles by likelihood of play will fill the page with
already-watched titles è Poor member experience
§ Recommendations for different modes have different
sensitivities to member actions
§ Continuation recs may react immediately to watching activities,
My List recs may react to My List add/remove activities, etc.
17
18. Approach 2: Dedicated Models + Blend
§ Build separate models for the each mode
§ Blend the results on the page
§ Blending can be done through a model trained offline, or a
parameter tuned online
§ E.g., one or more dedicated rows for each mode
§ Pro:
§ More modular, provides more intuitive knobs for balancing
§ Con:
§ Less elegant, more maintenance 18
20. Continue Watching Row: The Past
§ CW row was shown on some devices
§ Videos sorted by recency of last watch
§ Row appearance on page by business rules
§ On the website, only a single CW title
§ A very significant fraction of plays are continuations
§ CW deserved a better treatment
20
21. Objective
§ Unify the CW row across devices
§ Optimize the row in two dimensions:
§ Row position on page
§ Place it higher when the member is more
likely to resume a video
§ Re-order the titles within the CW row
§ By their likelihood to be resumed in the
current session
21
22. Some Intuitive Patterns
§ Member may be more likely to want to
§ Resume a video if:
§ In the middle of binging a TV show
§ Partially watched a movie recently
§ Often watched it around this time of the day, location, or on the current
device
§ Discover a new title if:
§ Just finished a movie or completed all episodes of a show
§ Hasn’t watched anything recently
§ Is a relatively new member
22
23. Building a Recommendation Model for CW
§ Feature Brainstorm
§ Training Data
§ Models and Metrics
§ Implementation
23
24. Feature Ideas
§ Member-level:
§ Member’s subscription: tenure, country, language
§ How active has the member been recently
§ Member past ratings, genre preferences, etc.
24
25. Feature Ideas
§ Video and member’s previous interactions with it:
§ How recently was the video added to the catalog, watched, ...
§ How much of the movie/show watched
§ Video metadata:
§ Type and genre of video, # episodes
§ E.g., kids titles may be re-watched more
§ What else is on the catalog
§ Popularity and relevance of the video
§ How often do members resume this video
25
27. Title Ranking Model
§ Training data
§ Continuation sessions
§ Look at which of the recently-watched titles were played?
§ Model
§ Learn-to-rank: Linear/ensembles/…
§ Optimize for how well we rank the played title among other titles
27
28. Title Ranking Model: Performance
§ Baseline: Ranking by recency of
last play
§ Recency rank was also an
important feature in the model
§ Metrics significantly higher than
the baseline
§ E.g. Significant lift in precision
§ A/B testing also showed
improvements
28
29. Row Placement Model
§ Objective
§ Estimate the likelihood of continuation vs. discovery
§ Map that likelihood to a position on the page
§ Simplification:
§ Fix two candidate positions on the page and apply a threshold
§ Tune the threshold to optimize some accuracy metric
29
30. Row Placement Model: Training
§ Training data
§ Randomly select sessions with plays globally
§ Model
§ Binary classification of continuation vs. discovery sessions
§ Evaluated using classification and ranking metrics
30
31. Row Placement Model: Performance
§ Metrics
§ Achieved high classification metrics for predicting continuation vs
discovery
§ Error types:
§ False positives è CW occupies top of the page unnecessarily
§ False negative è Difficult for member to find the CW title
§ Placing the row
§ Threshold trades off FP and FN è Hard to tune offline
§ Tuned the threshold by A/B testing
31
32. Reusing the Title Ranking Model
§ Use the title-level scores
§ Calibrate scores to get probability Pt of continuation for each CW
title t
§ Aggregate into an overall probability of continuation
§ E.g., assuming independence:
PCW = 1 - ∏tϵCW (1- Pt)
§ Pro: Avoids maintaining two separate models
§ Con: Not as accurate as a dedicated model
32
33. Context Awareness
§ Title ranks highest on the same time of day and device
as last play
§ Experiment:
§ Played “Sid the Science Kid” on iPhone
§ Played “Narcos” on the website
è Different ranking on iPhone and Web
33
34. Serving the CW Row in Production
§ Score cannot be precomputed è Real- or near real-time
§ Some features are context dependent
§ Row should refresh each time a member watches a title
§ Need to push updates to clients to keep the row fresh
§ Latency bottleneck: Data transfers from the cache to
computation backend
§ Requires careful backend engineering
§ Fallback strategy: If computation fails, can use recency ranking
34
36. Conclusions
§ Important to understand different modes of behavior
§ Continuation is a key driver of streaming hours
§ Improving CW recommendations improves member experience
§ A/B testing showed significant boost in user engagement
§ Future:
§ Incorporate the placement of CW row (and others) into the main
page construction model
§ When can we automatically start resuming a title? 36