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.
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.
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.
(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 try to provide the best personalized video recommendations to our members. To do this, we need to adapt our recommendations for each contextual situation, which depends on information such as time or device. In this talk, I will describe how state of the art Contextual Recommendations are used at Netflix. A first example of contextual adaptation is the model that powers the Continue Watching row. It uses a feature-based approach with a carefully constructed training set to learn how to adapt to the context of the member. Next, I will dive into more modern approaches such as Tensor Factorization and LSTMs and share some results from deployments of these methods. I will highlight lessons learned and some common pitfalls of using these powerful methods in industrial scale systems. Finally, I will touch upon system reliability, choice of optimization metrics, hidden costs, risks and benefits of using highly adaptive systems.
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.
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.
(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 try to provide the best personalized video recommendations to our members. To do this, we need to adapt our recommendations for each contextual situation, which depends on information such as time or device. In this talk, I will describe how state of the art Contextual Recommendations are used at Netflix. A first example of contextual adaptation is the model that powers the Continue Watching row. It uses a feature-based approach with a carefully constructed training set to learn how to adapt to the context of the member. Next, I will dive into more modern approaches such as Tensor Factorization and LSTMs and share some results from deployments of these methods. I will highlight lessons learned and some common pitfalls of using these powerful methods in industrial scale systems. Finally, I will touch upon system reliability, choice of optimization metrics, hidden costs, risks and benefits of using highly adaptive systems.
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.
The Netflix experience is driven by a number of Machine Learning algorithms: personalized ranking, page generation, search, similarity, ratings, etc. On the 6th of January, we simultaneously launched Netflix in 130 new countries around the world, which brings the total to over 190 countries. Preparing for such a rapid expansion while ensuring each algorithm was ready to work seamlessly created new challenges for our recommendation and search teams. In this post, we highlight the four most interesting challenges we’ve encountered in making our algorithms operate globally and, most importantly, how this improved our ability to connect members worldwide with stories they'll love.
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.
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.
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.
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.
Artwork Personalization at Netflix Fernando Amat RecSys2018 Fernando Amat
For many years, the main goal of the Netflix personalized recommendation system has been to get the right titles in front of our members at the right time. But the job of recommendation does not end there. The homepage should be able to convey to the member enough evidence of why a title may be good for her, especially for shows that the member has never heard of. One way to address this challenge is to personalize the way we portray the titles on our service. An important aspect of how to portray titles is through the artwork or imagery we display to visually represent each title. The artwork may highlight an actor that you recognize, capture an exciting moment like a car chase, or contain a dramatic scene that conveys the essence of a movie or show. It is important to select good artwork because it may be the first time a member becomes aware of a title (and sometimes the only time), so it must speak to them in a meaningful way. In this talk, we will present an approach for personalizing the artwork we use on the Netflix homepage. The system selects an image for each member and video to give better visual evidence for why the title might be appealing to that particular member.
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!
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.
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.
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.
Recommendation systems today are widely used across many applications such as in multimedia content platforms, social networks, and ecommerce, to provide suggestions to users that are most likely to fulfill their needs, thereby improving the user experience. Academic research, to date, largely focuses on the performance of recommendation models in terms of ranking quality or accuracy measures, which often don’t directly translate into improvements in the real-world. In this talk, we present some of the most interesting challenges that we face in the personalization efforts at Netflix. The goal of this talk is to sunshine challenging research problems in industrial recommendation systems and start a conversation about exciting areas of future research.
Lessons Learned from Building Machine Learning Software at NetflixJustin Basilico
Talk from Software Engineering for Machine Learning Workshop (SW4ML) at the Neural Information Processing Systems (NIPS) 2014 conference in Montreal, Canada on 2014-12-13.
Abstract:
Building a real system that incorporates machine learning as a part can be a difficult effort, both in terms of the algorithmic and engineering challenges involved. In this talk I will focus on the engineering side and discuss some of the practical issues we’ve encountered in developing real machine learning systems at Netflix and some of the lessons we’ve learned over time. I will describe our approach for building machine learning systems and how it comes from a desire to balance many different, and sometimes conflicting, requirements such as handling large volumes of data, choosing and adapting good algorithms, keeping recommendations fresh and accurate, remaining responsive to user actions, and also being flexible to accommodate research and experimentation. I will focus on what it takes to put machine learning into a real system that works in a feedback loop with our users and how that imposes different requirements and a different focus than doing machine learning only within a lab environment. I will address the particular software engineering challenges that we’ve faced in running our algorithms at scale in the cloud. I will also mention some simple design patterns that we’ve fond to be useful across a wide variety of machine-learned systems.
RecSys 2020 A Human Perspective on Algorithmic Similarity Schendel 9-2020Zachary Schendel
In the Netflix user interface (UI), when a row or UI element is named “Because you Watched...”, “More Like This”, or “Because you added to your list”, the overarching goal is to recommend a movie or TV show that a member might like based on the fact that they took a meaningful action on a source item. We have employed similar recommendations in many UI elements: on the homepage as a row of recommendations, after you click into a title, or as a piece of information about why a member should watch a title.
From an algorithmic perspective, there are many ways to define a “successful” similar recommendation. We sought to broaden that definition of success. To this end, the Consumer Insights team recently completed a suite of research projects to explore the intricacies of member perceptions of similar recommendations. The Netflix Consumer Insights team employs qualitative (e.g., in-depth interviews) and quantitative (e.g., surveys) research methods, interfacing directly with Netflix members to uncover pain points that can inspire new product innovation. The research concluded that, while the typical member believes movies are broadly similar when they share a common genre or theme, similarity is more complex, nuanced, and personal than we might have imagined. The vernacular we use in the UI implies that there should be at least some kind of relationship between the source item and the recommendations that follow. Many of our similar recommendations felt “out of place”, mostly because the relationship between the source item and the recommendation was unclear or absent. When similar recommendations tell a completely misleading, incorrect, or confusing story, member trust can be broken.
We will structure the presentation around three new insights that our research found to have an influence on the perception of similarity in the context of Netflix as well as the research methods used to uncover those insights. First, the reason a member loves a given movie will vary. For example, do you want to watch other baseball movies like Field of Dreams, or would you prefer other romances like Field of Dreams? Second, members are more or less flexible about how similar a recommendation actually needs to be depending on the properties of and their interactions with the canvas containing the recommendation. For example, a Because You Watched row on the homepage implies vaguer similarity while a More Like This gallery behind a click into the source item implies stricter similarity. Finally, even when we held the UI element constant, we found that similar recommendations are only valuable in some contexts. After finishing a movie, a member might prefer a similar recommendation one day and a change of pace the next. Research methods discussed will include Inverse Multi-Dimensional Scaling [1], survey experimentation, and ways to apply qualitative research to improve algorithmic recommendations.
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.
Anoop Deoras - Building an Incrementally Trained, Local Taste Aware, Global D...MLconf
Building an Incrementally Trained, Local Taste Aware, Global Deep Learned Recommender System Model
At Netflix, our main goal is to maximize our members’ enjoyment of the selected show by minimizing the amount of time it takes for them to find it. We try to achieve this goal by personalizing almost all the aspects of our product -- from what shows to recommend, to how to present these shows and construct their home-pages to what images to select per show, among many other things. Everything is recommendations for us and as an applied Machine Learning group, we spend our time building models for personalization that will eventually increase the joy and satisfaction of our members. In this talk we will primarily focus our attention on a) making a global deep learned recommender model that is regional tastes and popularity aware and b) adapting this model to changing taste preferences as well as dynamic catalog availability.
We will first go through some standard recommender system models that use Matrix Factorization and Topic Models and then compare and contrast them with more powerful and higher capacity deep learning based models such as sequence models that use recurrent neural networks. We will show what it entails to build a global model that is aware of regional taste preferences and catalog availability. We will show how models that are built on simple Maximum Likelihood principle fail to do that. We will then describe one solution that we have employed in order to enable the global deep learned models to focus their attention on capturing regional taste preferences and changing catalog.In the latter half of the talk, we will discuss how we do incremental learning of deep learned recommender system models. Why do we need to do that ? Everything changes with time. Users’ tastes change with time. What’s available on Netflix and what’s popular also change over time. Therefore, updating or improving recommendation systems over time is necessary to bring more joy to users. In addition to how we apply incremental learning, we will discuss some of the challenges we face involving large-scale data preparation, infrastructure setup for incremental model training as well as pipeline scheduling. The incremental training enables us to serve fresher models trained on fresher and larger amounts of data. This helps our recommender system to nicely and quickly adapt to catalog and users’ taste changes, and improve overall performance.
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.
The Netflix experience is driven by a number of Machine Learning algorithms: personalized ranking, page generation, search, similarity, ratings, etc. On the 6th of January, we simultaneously launched Netflix in 130 new countries around the world, which brings the total to over 190 countries. Preparing for such a rapid expansion while ensuring each algorithm was ready to work seamlessly created new challenges for our recommendation and search teams. In this post, we highlight the four most interesting challenges we’ve encountered in making our algorithms operate globally and, most importantly, how this improved our ability to connect members worldwide with stories they'll love.
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.
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.
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.
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.
Artwork Personalization at Netflix Fernando Amat RecSys2018 Fernando Amat
For many years, the main goal of the Netflix personalized recommendation system has been to get the right titles in front of our members at the right time. But the job of recommendation does not end there. The homepage should be able to convey to the member enough evidence of why a title may be good for her, especially for shows that the member has never heard of. One way to address this challenge is to personalize the way we portray the titles on our service. An important aspect of how to portray titles is through the artwork or imagery we display to visually represent each title. The artwork may highlight an actor that you recognize, capture an exciting moment like a car chase, or contain a dramatic scene that conveys the essence of a movie or show. It is important to select good artwork because it may be the first time a member becomes aware of a title (and sometimes the only time), so it must speak to them in a meaningful way. In this talk, we will present an approach for personalizing the artwork we use on the Netflix homepage. The system selects an image for each member and video to give better visual evidence for why the title might be appealing to that particular member.
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!
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.
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.
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.
Recommendation systems today are widely used across many applications such as in multimedia content platforms, social networks, and ecommerce, to provide suggestions to users that are most likely to fulfill their needs, thereby improving the user experience. Academic research, to date, largely focuses on the performance of recommendation models in terms of ranking quality or accuracy measures, which often don’t directly translate into improvements in the real-world. In this talk, we present some of the most interesting challenges that we face in the personalization efforts at Netflix. The goal of this talk is to sunshine challenging research problems in industrial recommendation systems and start a conversation about exciting areas of future research.
Lessons Learned from Building Machine Learning Software at NetflixJustin Basilico
Talk from Software Engineering for Machine Learning Workshop (SW4ML) at the Neural Information Processing Systems (NIPS) 2014 conference in Montreal, Canada on 2014-12-13.
Abstract:
Building a real system that incorporates machine learning as a part can be a difficult effort, both in terms of the algorithmic and engineering challenges involved. In this talk I will focus on the engineering side and discuss some of the practical issues we’ve encountered in developing real machine learning systems at Netflix and some of the lessons we’ve learned over time. I will describe our approach for building machine learning systems and how it comes from a desire to balance many different, and sometimes conflicting, requirements such as handling large volumes of data, choosing and adapting good algorithms, keeping recommendations fresh and accurate, remaining responsive to user actions, and also being flexible to accommodate research and experimentation. I will focus on what it takes to put machine learning into a real system that works in a feedback loop with our users and how that imposes different requirements and a different focus than doing machine learning only within a lab environment. I will address the particular software engineering challenges that we’ve faced in running our algorithms at scale in the cloud. I will also mention some simple design patterns that we’ve fond to be useful across a wide variety of machine-learned systems.
RecSys 2020 A Human Perspective on Algorithmic Similarity Schendel 9-2020Zachary Schendel
In the Netflix user interface (UI), when a row or UI element is named “Because you Watched...”, “More Like This”, or “Because you added to your list”, the overarching goal is to recommend a movie or TV show that a member might like based on the fact that they took a meaningful action on a source item. We have employed similar recommendations in many UI elements: on the homepage as a row of recommendations, after you click into a title, or as a piece of information about why a member should watch a title.
From an algorithmic perspective, there are many ways to define a “successful” similar recommendation. We sought to broaden that definition of success. To this end, the Consumer Insights team recently completed a suite of research projects to explore the intricacies of member perceptions of similar recommendations. The Netflix Consumer Insights team employs qualitative (e.g., in-depth interviews) and quantitative (e.g., surveys) research methods, interfacing directly with Netflix members to uncover pain points that can inspire new product innovation. The research concluded that, while the typical member believes movies are broadly similar when they share a common genre or theme, similarity is more complex, nuanced, and personal than we might have imagined. The vernacular we use in the UI implies that there should be at least some kind of relationship between the source item and the recommendations that follow. Many of our similar recommendations felt “out of place”, mostly because the relationship between the source item and the recommendation was unclear or absent. When similar recommendations tell a completely misleading, incorrect, or confusing story, member trust can be broken.
We will structure the presentation around three new insights that our research found to have an influence on the perception of similarity in the context of Netflix as well as the research methods used to uncover those insights. First, the reason a member loves a given movie will vary. For example, do you want to watch other baseball movies like Field of Dreams, or would you prefer other romances like Field of Dreams? Second, members are more or less flexible about how similar a recommendation actually needs to be depending on the properties of and their interactions with the canvas containing the recommendation. For example, a Because You Watched row on the homepage implies vaguer similarity while a More Like This gallery behind a click into the source item implies stricter similarity. Finally, even when we held the UI element constant, we found that similar recommendations are only valuable in some contexts. After finishing a movie, a member might prefer a similar recommendation one day and a change of pace the next. Research methods discussed will include Inverse Multi-Dimensional Scaling [1], survey experimentation, and ways to apply qualitative research to improve algorithmic recommendations.
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.
Anoop Deoras - Building an Incrementally Trained, Local Taste Aware, Global D...MLconf
Building an Incrementally Trained, Local Taste Aware, Global Deep Learned Recommender System Model
At Netflix, our main goal is to maximize our members’ enjoyment of the selected show by minimizing the amount of time it takes for them to find it. We try to achieve this goal by personalizing almost all the aspects of our product -- from what shows to recommend, to how to present these shows and construct their home-pages to what images to select per show, among many other things. Everything is recommendations for us and as an applied Machine Learning group, we spend our time building models for personalization that will eventually increase the joy and satisfaction of our members. In this talk we will primarily focus our attention on a) making a global deep learned recommender model that is regional tastes and popularity aware and b) adapting this model to changing taste preferences as well as dynamic catalog availability.
We will first go through some standard recommender system models that use Matrix Factorization and Topic Models and then compare and contrast them with more powerful and higher capacity deep learning based models such as sequence models that use recurrent neural networks. We will show what it entails to build a global model that is aware of regional taste preferences and catalog availability. We will show how models that are built on simple Maximum Likelihood principle fail to do that. We will then describe one solution that we have employed in order to enable the global deep learned models to focus their attention on capturing regional taste preferences and changing catalog.In the latter half of the talk, we will discuss how we do incremental learning of deep learned recommender system models. Why do we need to do that ? Everything changes with time. Users’ tastes change with time. What’s available on Netflix and what’s popular also change over time. Therefore, updating or improving recommendation systems over time is necessary to bring more joy to users. In addition to how we apply incremental learning, we will discuss some of the challenges we face involving large-scale data preparation, infrastructure setup for incremental model training as well as pipeline scheduling. The incremental training enables us to serve fresher models trained on fresher and larger amounts of data. This helps our recommender system to nicely and quickly adapt to catalog and users’ taste changes, and improve overall performance.
Deep learning has accomplished impressive feats in areas such as voice recognition, image processing, and natural language processing. Deep learning enthusiasts have rushed to predict that this family of algorithms is likely to take over most other applications in the near future. This focus on deep architectures seems to have cast a shadow over more “traditional” machine learning and data science approaches, leaving researchers and practitioners alike wondering whether there is any point in investing in feature engineering or simpler models.
In this talk, I will go over what deep learning can and cannot do for you, both now and in the near future. I will also describe how different approaches will continue to be needed, and why their demand will likely grow despite the rise of deep learning. I will support my claims not only by looking at recent publications, but also by using practical examples drawn from my experience at companies at the forefront of machine learning applications, such as Quora.
Adam Kramarzewski is a Game Designer at Space Ape with 11 years of experience in the industry and a new book just about to be published. He gives students an unfiltered insight into the production practices, responsibilities, and challenges facing Game Designers in the modern game development scene.
Powerpoint used during the 2012 - Minnesota Evaluation Society Institute (MESI). For more information about the project and useful handouts visit http://z.umn.edu/onlinefocusgroups
The Shitposting AI With Thomas Endres & Jonas Mayer | Current 2022HostedbyConfluent
Using modern AI approaches such as GPT-2, Tacotron and Conformers, we created fully autonomous robot heads that engage in heated social media discussions, completely taking the human out of the loop. The TNG Innovation Hacking Team created a prototype of an end-to-end natural language understanding system, employing techniques such as Speech-to-Text (STT), Conditional Text Generation and Text-To-Speech (TTS).
Social media comments have become the predominant medium for public discussion. However, discussions on Facebook, Twitter and Reddit are notorious for their poor debate culture and missing conclusiveness. The obvious solution to this tremendous waste of time is automation of such fruitless discussions using a bot.
In this talk, we will give an introduction to NLP, focussing on the concepts of STT, Text Generation and TTS. Using live demos, we will guide you through the process of scraping social media comments, training a text generation model, synthesizing millions of voices and building IoT robot heads.
Real-time personalized recommendations using product embeddingsJakub Macina
Presented at PyCon.
Recommender systems are successfully used in several domains, e.g. product or movie recommendation. In e-commerce, the aim is to provide personalized suggestions to users for relevant items out of all products available.
In this talk, I will focus on item recommendation for anonymous users when no historical data about the user is available (also referred as a cold-start problem) and challenges we have encountered. Firstly, I will dig deeper into similar item recommendation by NLP model comparing textual descriptions of items. This approach is based on word embeddings extracted from neural network models, such as word2vec or fasttext.
Finally, I will talk about how to apply the same idea of word embeddings to learn a representation of each product. With the product embedding representation, it is easy to calculate similarities between products in real-time. Moreover, we found out that product embeddings are able to capture the style of a product, color, category or a price level.
All of the examples will be practical using data about restaurants reviews and fashion products. Open-source NLP library Gensim is used in code samples. The presentation will be supported by visualization of embeddings to get the idea behind. Everybody with any interest in machine learning is welcome. After the presentation, you will know how to compute the relationship between pizza and pasta or how to capture a fashion style of a user.
Deep learning: the future of recommendationsBalázs Hidasi
An informative talk about deep learning and its potential uses in recommender systems. Presented at the Budapest Startup Safary, 21 April, 2016.
The breakthroughs of the last decade in neural network research and the quick increasing of computational power resulted in the revival of deep neural networks and the field focusing on their training: deep learning. Deep learning methods have succeeded in complex tasks where other machine learning methods have failed, such as computer vision and natural language processing. Recently deep learning has began to gain ground in recommender systems as well. This talk introduces deep learning and its applications, with emphasis on how deep learning methods can solve long standing recommendation problems.
Xavier Amatriain, VP of Engineering, Quora at MLconf SF - 11/13/15MLconf
10 More Lessons Learned from Building Real-Life ML Systems: A year ago I presented a collection of 10 lessons in MLConf. These goal of the presentation was to highlight some of the practical issues that ML practitioners encounter in the field, many of which are not included in traditional textbooks and courses. The original 10 lessons included some related to issues such as feature complexity, sampling, regularization, distributing/parallelizing algorithms, or how to think about offline vs. online computation.
Since that presentation and associated material was published, I have been asked to complement it with more/newer material. In this talk I will present 10 new lessons that not only build upon the original ones, but also relate to my recent experiences at Quora. I will talk about the importance of metrics, training data, and debuggability of ML systems. I will also describe how to combine supervised and non-supervised approaches or the role of ensembles in practical ML systems.
Event Management System Vb Net Project Report.pdfKamal Acharya
In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on.
My project named “Event Management System” is software that store and maintained all events coordinated in college. It also helpful to print related reports. My project will help to record the events coordinated by faculties with their Name, Event subject, date & details in an efficient & effective ways.
In my system we have to make a system by which a user can record all events coordinated by a particular faculty. In our proposed system some more featured are added which differs it from the existing system such as security.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
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Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
2. ● Personalization and Recommendations at Netflix
● Discuss evolution of latent models in the Recommender System space
● Showcase some experimental results and interesting findings
● Take away points
Theme of the talk
5. ● Recommendation Systems are means to an end.
● Our primary goal:
○ Maximize Netflix member’s enjoyment of the selected show
■ Enjoyment integrated over time
○ Minimize the time it takes to find them
■ Interaction cost integrated over time
Personalization
● Personalization
6. Ordering of the titles in each row is personalized
From what shows to recommend
14. Personalization and Its Design
Considerations
● A good Recommender Systems should therefore consider:
○ What to recommended
■ Relevant content appeals to our members
○ How to recommended
■ Appealing presentation increases their joy
15. What and How to Model
● We try to model
○ User’s taste
○ Context
■ Time
■ Device
■ Country
■ Language
■ …
○ Difference in catalogue and local tastes
■ What is popular in US may not be popular in India
■ Not available != Not Popular
○ Presentation
16. What and How to Model
● We try to model
○ User’s taste
○ Context
■ Time
■ Device
■ Country
■ Language
■ …
○ Difference in catalogue and local tastes
■ What is popular in US may not be popular in India
■ Not available != Not Popular
○ Presentation
18. Basic Intuition
● Imagine you walked into a room full of movie enthusiasts, from all over the world,
from all walks of life, and your goal was to come out with a great movie
recommendation.
● Would you obtain popular vote ? Would that satisfy you ?
19. Basic Intuition
● Now consider forming groups of people with similar taste based on the videos that
they previously enjoyed.
20.
21.
22.
23. Basic Intuition
● Describe yourself using what you have watched.
● Try to associate yourself with these groups and obtain a weighted “personalized”
popularity vote.
29. Topic Models (Latent Dirichlet Alloc)
K
U
P
α θ φt v
β
Total
Topics
Taste
Convex Combinations of
topics proportions and movie
proportions within topic
30. Topic Models (LDA): Scoring
P
Q
Users
Videos Distribution
over topics
for user-i
Topic
Conditional
distribution for
video-j
33. Neural Multi Class Models
play (t-n)
...
play (t-1)
cntxt
Soft-max over entire vocabulary
play
(t-n)...
play
(t-1)cntxt
Soft-max over entire vocabulary
N-GRAM BoW-n
Feed
Forward User,Cntxt
P(next-video | <user, cntxt>)
34. Neural Multi Class Models
play
(t-1)
cntxt
Soft-max over entire vocabulary
state
(t-1)
RNN Family
play
(t-2)
...
play
(t-1)
Soft-max over entire vocabulary
cntxt
play
(t-4)play
(t-3)
play
(t-n)play
(t-n+1)
CNN Family
state
(t)
Recurrent
Convolutn
P(next-video | <user, cntxt>)
36. Interpreting a CNN CF Model
● Deeper CNN layers have discovered higher level features in images:
○ Edges
○ Faces etc
● What would a CNN learn if it is trained on user-item interaction dataset?
○ Can it discover semantic topics ?
39. Take Away Points
● Linear models
○ Presented a unified view of various latent factor models
○ Discussed limited modeling capacity ⇒ inferior prediction power
● Non-Linear (Deep Learning) models
○ Encoding of rich nonlinear user item interaction ⇒ superior prediction power
○ Discussed how VAEs can be thought of as non linear LDA
○ Showcased how ‘Next Play models’ model directly the task at hand
40. Some challenging Problems
● Modeling User Context in these frameworks
● Modeling differences in local tastes and catalog differences
○ How to impute for missing plays
○ Censored cross entropy loss
● Unification of various recommender systems
○ Movie recommender, Page builder, Art work selector and many more
● How to minimize production bias
○ Correlation is not Causation
● Long term reward -- User joy
○ Reinforcement Learning
