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Getting your Amazon Personalize Recommendations in Front of your Users

hello and welcome to the next episode in,the amazon personalized deep dive series,in this episode we will be looking at,how to get recommendations from the,models you create and personalize,and some patterns for integrating,recommendations into your application,my name is james jory i'm an ai,solutions architect with aws,and i will be leading you through this,episode,before jumping into the topic for this,episode i wanted to quickly recap where,we are in the video series,we've broken up the series into four,areas that generally map to the,developer journey with the service,the series dives progressively deeper on,topics ranging from how to prepare and,manage your data for personalize,how to select and configure the,appropriate recipes or algorithms,for your use cases how to train models,how to retrieve recommendations from,your models to integrate them into your,application,and how to operationalize personalized,for production,if you're just getting started with,personalize we recommend starting with,the introduction video,and watching the series in order,in this episode we will be focusing on,the topic of inference,inference is the machine learning term,for asking a model to make a prediction,in the case of personalized this is,where we get recommendations from the,solution,or more specifically the solution,version you created based on your data,so with that let's dive in,there are two general approaches to,getting recommendations from personalize,real-time recommendations are best,combined with interactive user,experiences,such as pages on your website or views,in your mobile application,where users are browsing and interacting,with content,the apis used to get recommendations in,real time are transactional in nature,meaning they're typically called in,response to some sort of user activity,in your application,there are two real-time apis get,recommendations,and get personalized ranking the one you,call depends on the underlying use case,and the recipe that you chose for that,use case,we will go into more detail on these,apis in this episode,the real-time apis are synchronous low,latency and auto-scaling,this means they follow the request,response model are designed to be,integrated as part of the flow of,rendering your user experiences,and will adapt to the traffic patterns,of your application by auto-scaling,resources as needed,the runtime requirements are implemented,in what's called a personalized campaign,you create a campaign for each solution,or model that you train,the real-time apis also have the ability,to adapt recommendations to meet your,users,changing interests and intent this is,only supported for the user,personalization,personalized ranking and the legacy hr,and n recipes,and you must stream new interactions,into an event tracker to enable,this behavior in cases where the current,context of the user influences their,behavior,you can provide relevant context,information in your api call to help,personalize make even more relevan

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10 Predictions For 2023 | Animal Spirits 290

oh,welcome to Animal Spirits with Michael,and Ben,I'm I'm a I'm a basic,okay how so,because when the new year turns over so,does my uh so does my energy I am one of,those people who says you know what,January 1st going on a diet,that's good because last week to start,off the show you were a little low,energy you needed to pick me up I really,was I was low energy,um,I know you stay in shape throughout the,year but I I don't know I certainly let,the holidays I I ate a lot I was eating,a lot of pizza and french fries and,burgers and sweets so I'm uh I don't,like to give a lot of like Fitness,influencer advice,but if I had any advice for anyone it,would just be that like have some sort,of dietary system that you can easily go,back to because I think Chick-fil-A at,10 am well by the way I I had,Chick-fil-A for the first time in a long,time the other day uh I had a spicy,chicken sandwich,it's good but in my opinion when these,spicy chicken is still the king okay I,don't want to get into fast food debate,here I'm just My Hope sounds like it,sounds like you agree,like especially during the week have,four to five things that you can eat on,a regular basis that you don't deviate,from that's how you that's how you keep,your food intake to a a reasonable level,so here's your Fitness advice here's,what I'm trying to accomplish in the,month of January,uh I'm gonna cut out carbs,I was gonna do the Whole 30 but come on,I need Dairy that's that's ridiculous,I'm just gonna cut our carbs no soda no,sweets nothing like that I'm just gonna,keep it simple,and I'm also going to try and cut out,booze but the problem is I've got I'm,going to stop you right there that's too,much,give yourself a cheat day you can't go,from like sitting in the couch to,running a marathon you have to Baby,Steps give yourself a cheat day at least,a cheetah no I can't do a cheat day,because if I fall off I fall off hard I,could do 30 days but the the rock gives,himself a cheap meal do you like be like,the Rock post it on Instagram,I'm a little bit worried about the booze,because I checked my calendar and I've,got a few dinners lined up in New York,City,and,if I'm like out with my wife another,couple I have no problem not drinking,but it's hard when you have like people,coming into town it's like you know I,don't want to be like weird and but and,also also I have a potential trip lined,up to Minnesota,to see the Giants and there's a zero,percent chance I'm not drinking at the,job you can't go to a monster truck,rally without drinking a beer,like that although although there I,there was a lot of uh chatter about what,should I do now credit to the listeners,who informed me I'm a bit embarrassed,that the Saturday divisional games don't,start till three o'clock or maybe even 3,30. so I am in luck because the monster,truck thing starts at 12. no harm no,foul good to go,should we do a little year-end review,here I've been doing that last couple,days question for you question for you,I'm pretty sure I

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Insider Tips for Building Personalized Recommender Systems

all right let's get started and talk,about recommender systems guys so uh let,me give you a little bit more about my,bio and we'll open it up for questions,and if there's no questions from the,audience here i have some frequently,asked questions that i can go through as,well that i'm prepared to talk about and,uh we can also go through some code and,actually see a real working recommender,system in action so we got plenty of,material to fill up this time i think,uh but yeah just to tell you a little,bit about me first,let's talk about me,my name is frank kane again i am uh the,president of sundog education and uh,these days i earn a living by making,online video courses on platforms such,as manning,um before that though i spent,nine years working at amazon.com started,there in 2003 as a senior engineer,software engineer and when i started,there they threw me into what they call,the personalization team this was the,guys that were doing personalized,recommendations people who bought also,bought and back then they didn't really,call them recommender systems you know,it wasn't really a thing yet it's just,it was just algorithms it was uh,programming more than anything you know,so we were kind of like blending these,fields of uh you know data science,although it wasn't really call data,science back then uh software,engineering and system engineering at,very large scale and distributed systems,to try to compute these recommendations,for our customers,at least on a daily basis and serve that,in real time you know to thousands and,thousands of transactions per second it,was an exciting place and kind of,learned by getting thrown into the into,the deep end of the pool there you know,but um,you know i managed to swim thankfully,and uh worked my way up to senior,manager by the time i left there um by,the time i was done with my career at,amazon i was actually running the,engineering team for imdb.com which is a,big movie website that's a subsidiary of,amazon lots of fun there like it's it's,just as fun as it sounds,um,but after nine years um my family just,couldn't take the weather in seattle,anymore so we packed up cashed out and,moved to florida that's where we are now,and i've been working for myself ever,since making courses for you so that's,my expertise in recommender systems um i,didn't just do recommender systems at,amazon but it really was the focus of,what i did for most of that time so,between recommender systems and content,optimization and you know larger scale,system engineering work but,personalization was always what it,seemed to come back to for me even at,imdb we had a movie recommender system,and you'll see the the dna of that,experience in my course here because we,lean heavily on making movie,recommendations in this course about,recommender systems because there's some,really fun data sets out there to play,with on that,hey we've got a question and it sounds,like a hard one,how can you determine the minimal size,of previous information

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AWS re:Invent 2019: Delight your customers with ML-based personalized recommendations (AIM323)

hi everyone welcome to arraignment I,hope you're having a good day one,my name is Vaibhav city I'm a senior,product manager on Amazon personalized,and today we are going to be talking,about how to use how to create,personalized user experiences using,machine learning why Amazon personalized,in a short while I'm going to be joined,on the stage by I'm Eric and Robin from,voodoo and from Marc from Coursera so,voodoo and Coursera are our customers,then you're going to be hearing more,about how they have been using Amazon,personalized so this is a special event,for us for all of us in the personalized,team Amazon personalized was launched as,a preview service at reinvent last year,we went GA only this year and we have,been very encouraged by the kind of,success we have seen customers achieve,with the service and we have two of the,such customers to talk about it today so,let's have a brief look at the agenda so,we start off with a brief overview of,the business impact of personalization,followed by a gentle introduction to,personalize then we're going to go a bit,deeper into how you can apply Amazon,personalized to a couple of,personalization use cases then we're,going to have you do talk comment stage,and talk about how they're using Amazon,personalized for their in-app,recommendations and how Coursera then,Coursera has been using personalized for,improving course subscription revenue,and then we followed by a Q&A so let's,get started why personalization so,personalization has several material,business benefits the first business,benefit is that it's a very very elegant,way of letting customers discover more,of your tail catalog so if you have a,large product or item catalog and you,want customers to find relevant items in,there,organically personalization is a good,way to do that personalization is also,empirically proven to improve customer,engagement so this is typically measured,in terms of CTR in terms of watch,duration or,videos if you are doing video,personalization in terms of dwell times,on articles and all this leads to,increased engagement by the users so,typically personalized user experiences,tend to have a higher conversion rate so,for example higher app downloads higher,number of people subscribing for your,services and this finally leads to,increase in revenue which is a metric I,am sure all of us care about so the,business benefit of personalization is,fairly obvious right but the part to,achieve this business benefit is not,very obvious so we have been doing,personalization on amazon.com for a,while so we started way back in 1998,with fairly simple customer experiences,so this is an example of item to item,collaborative filtering algorithm,powered recommendations so seeing what,customers have in the cart we recommend,other items right so these kind of user,experiences were very well loved by our,customers and from there we started on a,journey of adding more and more,personalization to all kinds of user,experiences on amazon.com on

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How to deliver personalized recommendations using Amazon SageMaker

hi,my name is ben addicts and today i will,show you how to create,personalized recommendation using,machine learning on amazon sagemaker,amazon sagemaker helps data scientists,and developers,to quickly prepare build train,and deploy machine learning models by,bringing together a broad set,of purpose-built capabilities for this,demo,we'll be working in sagemaker studio,which is a fully integrated development,environment for machine learning,personalization at the highest level,describes how an organization,delivers more customized interactions,and unique experiences for users,it is a means to meet user expectations,by delivering the right experience at,the right time,and the right place the concept of,personalization,although not new now offers,organizations the ability to improve,brand loyalty grow revenue and increase,efficiency,by using data to create a more,sophisticated and customized,customer experience with traditional,approaches to personalization,they're typically based on broad,segments of users,not tailored for every individual user,and therefore the recommendations,often times miss the mark machine,learning provides a scalable way to,deliver unique experiences to,individuals,based on their behavior and inferred,preferences rather than generic segments,of users,machine learning can help by processing,customer data and selecting the right,algorithms to dynamically present the,most relevant products,or content to each user at the right,time,for today's demo i will show how to use,sagemaker studio,lineage model registry and pipelines,to build train and deploy a personalized,recommendation engine using ecommerce,data,this will be a great opportunity for,data scientists and developers,new to aws and sagemaker studio to,explore some of the sagemaker features,in action,and be able to quickly create their own,personal solutions to fit their business,needs,to get started we'll need to launch,sagemaker studio,from within the sagemaker console we can,select,amazon sagemaker studio in the top left,and we'll be taken to the sagemaker,studio control panel,here we can see all sagemaker studio,users associated with our account,if there are no users you can create one,by clicking the add user icon,in the top right i already have a user,here so i will,click open studio and this will take us,to our integrated development,environment,just like that we're in a jupiter,environment and can begin our data,science development work,once we're in sagemaker studio we're,presented with this launcher,the launcher has a variety of different,features that we can take advantage of,for example we could start a new data,flow or we could start by creating our,own,jupyter notebook,we also have the files icon in the top,left where you can see any folders or,notebooks that are available,here it's empty because there are no,folders or files available,yet but for this demo we will need to,clone,the aws personalization repository,so to do so we can click the get icon,and then c

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Jakub Mačina - Real-time personalized recommendations using embeddings

so hello thank you very much for the,introduction in this talk I would like,to tell you how to create a recommender,system using machine learning based on,the data about how your customers are,browsing through your website so it's,first a little bit about me I graduated,from Seoul University of Technology and,I did my research in recommender systems,especially those used in education I did,the collaboration with EDX which is one,of the leading online courses provider,accept research I'm also open source,contributed to this course which is an,open source discussion platform and they,also spend google Summer of Code with,them also I co-founded projects pioneer,oh and participated in many different,competition with it and project is now -,open sourced and now I am working as an,engineer at Expo near so what will I,tell you today at first bit about,motivation for recommender systems then,some challenges we have encountered,building recommender system for,e-commerce and then I will dig deeper,into text base recommenders and product,embeddings so we are living in the era,of information overload do you know how,many of the 130 million books do you,know what to read next or what is the,best book for you to read or do you know,what will be your next summer heat out,of 35 million songs that is available at,Spotify yeah,that's a lot of data a lot of things to,consider by us and we are overloaded,there there is a recommender systems,that can help us recommender system,provides suggestions the user for items,that might be interested to consume or,item meetings their need or meeting,meeting their preferences or more,formally its,some machine learning model that,automatically tries to predict how user,will like a particular item what data we,can use for recommender systems usually,it is data about how your customers are,browsing through a website what is the,flow or what is their flow or we may use,data about the ratings,likes or reviews and based on that we,can compute some similarities between,users or between items so our conditions,are everywhere there are some businesses,that the core of their businesses is,based on recommendations like Netflix or,Spotify but also LinkedIn or steam so,what are we doing at exponent exponent,experience cloud and one part of it is,also recommendations as a service and,many of our clients are from ecommerce,and from different domains like fish,travel or telco and then why it's a very,challenging to build good,recommendations for them because what,works in one domain might not work in,other domain and why those businesses,are interested in recommendations and,because they would like to maximize,their business value yeah that's very,natural and also they would like to sell,more diverse items or sell new items on,stock and also personalization by,recommendation increased customer,experience their loyalty and,satisfaction so for example for Netflix,the value of recommendation is very high,because 80% of our stream and Netflix,come

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AWS On Air ft. Real-time Personalized Recommendations with Amazon Personalize

hello and welcome back to aws on air at,reinforced my name is marie carlton a,manager of partner solutions,architecture here at aws and i am joined,by the lovely brian over here,my name is brian terry and i'm a senior,developer advocate on the cloud,formation team and if this is the first,time you're joining us welcome today,we're joined by special guest a special,guest not a ghost at all,i'm not a ghost not a ghost not a ghost,or a pink footed platypus either oh yeah,so we're joined by special guest i'll,let him introduce himself hi my name is,raghavarov sodapathina i'm an enterprise,solution architect based out of boston,awesome awesome,so,what are we going to be talking about,today ross,i'm going to talk about uh how to build,a real-time personalized recommendations,by using amazon personalized super cool,and if you look at right many,organizations are they wanted to derive,the value from their data,and they started doing that building a,data lake where they can bring the data,from various sources,and then they do some analytics,analytics could be a real-time analytics,and it could be a batch analytics,now they want to invert their business,what it mean involved right because they,wanted to basically cross sell buy sell,and build a better customer experiences,right and how they can do it they have,to do like a data production that is,where machine learning and,artificial intelligence will come into,play right i mean if you really wanted,to build a machine learning it is still,evolving it's little complex and then,there is a lack of skill thus that is,where uh aws we have like many ai,services we just bring the data and then,we'll get you output,everything will be taken care by this,artificial intelligence services all,right so today i'm going to talk about,uh i want to talk about a little bit,more about,how,amazon personalized works,internally and then i will also,show the demo,um let me go back to the i have a couple,of slides which i want to run through,can you,show the slides uh i'm going to the,slides if you look at right,in if you wanted to build a machine,learning on your own right what you do,first you bring your data and then do,the algorithm right whether,you can choose,existing algorithm or you build your own,and then you have to do the trying and,test and then you have model ready,you get a prediction and then basically,you use that production output in your,application so that,you can show the recommendation it could,be a recommendation it could be a fraud,detection or it could be anything right,i wanna before we jump into kind of the,the life cycle here so for folks uh,watching at home how would we describe,amazon personalized to them yeah amazon,personalized is basically it will make,it easy for you you just bring your data,right your data means,it is meant for building a personalized,recommendation right and personalized,recommendation can be applied to many,industries it could be e-commerce it,could be a media and entertainment

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Trends in Recommendation & Personalization at Netflix

next up we're excited to welcome justin,basilico,justin basilico is the director of,machine learning and recommender systems,at netflix where he leads an applied,research team that creates the,algorithms used to personalize the,netflix homepage through machine,learning recommender systems and,large-scale software engineering,prior to netflix he worked in the,cognitive systems group at sandia,national laboratories he has an ms in,computer science from brown university,and a ba in computer science from pomona,college,justin,over to you,hi my name is justin i lead a machine,learning team here at netflix,working on the algorithms decide what,recommendations to show on people's,homepage,now having worked in netflix for over 10,years now there's a common set of,questions i get about the work we're,doing,and today i'm kind of going to go,through some of those questions and,really highlight you know some of the,recent areas of work we've been doing uh,here at netflix,so the first question i get from people,is kind of high level it's like well why,does netflix spend so much time uh,focusing on,personalization and,an answer to that is we really want to,help members find entertainment that,they really want to watch and they're,going to enjoy watching so we can,maximize their satisfaction and the,value of that they get on netflix and,then that in turn should hopefully,maximize uh their retention so they'll,stay customers with us and we can keep,making you know a better product and,more content uh to then kind of fuel,this this this nice cycle,but i like to think of it as beyond that,i really think about what we're trying,to do is trying to really spark joy with,people help them find that next great tv,show or movie that they absolutely love,and becomes their their new favorite um,and just make sure that you know they're,spending their time in the way that's,just uh that they get the most out of,their uh what they're watching,so in order to,achieve this goal what is it that we try,to personalize,the most obvious way you can see our,personalization is in how we rank items,on our service so there's rows like top,picks,where we rank things in a personalized,way but also things like the genres we,try to adapt based on and how we rank,them based on people's preferences,but we go beyond that and actually,construct the page and choose the rows,and the sets of recommendations in a,personalized way so that people can,easily browse and find a piece something,great to watch no matter what it is,they're in the mood for,we also take the search problem and go,beyond just doing uh textual search to,turn it into a recommendation,problem of helping them find given the,query you know what are the relevant set,of tvs and movies that,might be interesting for them,we've gone to also try out new modes of,interaction uh for example we recently,launched this play something feature,which is when people are having trouble,finding something to watch they can go,into this experience and

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Personalized Recommendations in Automation

if you're into sending your customers,personalized relevant offers that,deliver,value and convert then you're going to,love our newly released personalized,recommendations feature,and in this video i'm going to show you,the top three,automation workflows for bringing these,personalized recommendations,to your customers at the right time but,before we do that let's talk about how,and why,personalized recommendations actually,work personalized recommendations are,powered by an,algorithm that predicts the products,that are the most likely to be bought by,each individual customer,and these predictions rely on the,historical purchase data,across the entire customer base of your,brand,to illustrate here's a very simplified,example if customer a,buys a pair of jeans and customer b buys,that same pair of jeans,and a shirt that shirt is then going to,be recommended to customer a,consumers love merchants using,customer-centric tools like this,and the best part is that you don't need,to pick the items for your cross-sell or,upsell automations yourself,just drop in the product recommender,block into your email select the,personalized recommendations option,and you're good to go each of your,customers is now going to see the,recommendations that are unique to them,and are updated daily now let's look at,the automation workflows that we can use,to put this new feature,into practice number one shipping,confirmation,the reason this works so well is because,transactional emails such as order and,shipping confirmation,bring in the highest open rates by far,out of,all the different types of emails so,it's your perfect chance to showcase,and sell more relevant products and as i,said earlier just add the product,recommender block into your shipping,confirmation email,and you've just turned it into an,upselling machine,number two post purchase follow-up not,to be confused with order confirmation,a post-purchase follow-up email is when,you reach out to your customers about,seven days after they make a purchase,giving them some time to cool off,and making them more responsive to your,awesome personalized suggestions,you can even add a special incentive to,this follow-up email,to induce even more engagement and sales,number three browse or cart abandonment,for existing customers,your customers purchase history data can,not only help you personalize your post,purchase emails,but also improve the performance of your,browse and card recovery emails as well,but since you can only send personalized,recommendations to people who have,bought from you before,you will need to create a segment of,contacts whose number of orders placed,is at least one and then create an,automation split inside of your workflow,based on that segment,this is going to split all customers,into new and existing,and allow you to send personalized,recommendations to the existing ones,while using your regular recovery,strategy on the new visitors,personalized recommendations in,automation workflows are now

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RecSys 2016: Paper Session 1 - A Scalable Approach for Periodical Personalized Recommendations

it's a short paper so i might be a,little rushed to feel free to talk talk,to me often so I'm changing our pivot,generate a scalable approach to,theoretical personalized recommendations,it is John work with Asia and John from,TV master data science at Hollywood we,are interested in purity code,recommendations we should created,popular nowadays it could be Kate has,given converging driver for many in,commerce website me myself cut with the,emails from a customer strong Olympian,so I TV natural ability to connect plans,with labs events and personalized way,and it is well of our products so a,political recommendation is pretty,challenging to consider transitional,collaborative filtering and throughout,the major concerns could be account,about contact and novelty and these two,factors are kind of exaggerated,empirical recommendations for example,there could be domesticated contact,change across ways or mon month also if,we didn't until the novelty are,recommending are the same stop or,similar stuff across which could be a,disaster for users so there is a family,of algorithms Todd context of band-aids,for machine learning that it promised,promised in to address both both,problems just by looking at Nate look at,it's named the context of heart picture,of the complex a problem by using some,mercenary model and the bandits hard,uses some exploit exploration routines,to introduce novelty while trying to,learn a better model there is a large,literature here they say one major,category of contraband which algorithm,is randomization based on the best uses,for online applications with spots,feedback loops but for personal,recommendations we don't have first spot,feedback loops so the other category UCB,or upper confidence bounds types,reasons might be marketable so the great,idea here is when we try to predict,utility or saw a probability of clips or,convergence we do have some kind of,confidence interval and when we make a,prediction we will use it upper bound so,the incubation here is a week we try our,bus with with some clue while we do,exploration however most existing,algorithms are slower difficult,implement I in this paper we introduce a,new scalable approach that is open,source so when we think about confidence,interval twisting quad to striking from,stats 101 so when we have in front of,data we first time how to sampling with,replacement to generate a bunch of new,data set then with singer model on each,of them as prediction time we have a,bunch of predictions so we can do,whatever we want however such procedure,is difficult for big data problems and,is impossible for a streaming data with,unknown number alone here here come's,idea online with starting so the chaos,resolution here is the number of times,that each original data that's collected,in a new data set follow the binomial,distribution of n comma n and this,distribution converges to a Poisson,distribution rate one this is,interesting because this is it tells us,that we can eat it independentl

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