How does Netflix recommend movies? Matrix Factorization

[@Xraid32]

Sharknado = Twister + Jaws. This was gold. That was the moment all of Machine Learning made sense.

[@ianboard544]

It sounds like a pitch you might make to a production executive.

[@AmeyPanchpor02]

Very true

[@computerguycj1]

Sir, I've seen almost all of these concepts painfully "explained" in many different ways, but never have I seen them presented as elegantly and intuitively! Excellent video!

[@conintava514]

So informative and easy to follow. I love this. Thank you so much for taking the time to create this video. It's so important to know how the concepts we learn in class can be applied in real life. This has changed everything for me. Thank you again.

[@anushkagupta79]

I read so many articles about this topic but was never able to understand. You made it all so easy. Excellent work!!!

[@shivanshkaushik383]

This is a work of art. Never thought matrix factorization could be explained so effortlessly yet so clearly. You have helped me a lot with this sir! Thank You, God bless you!

[@atulitraopervatneni9320]

You are one of best teachers on KZbin. Thanks

[@reyhanehhashempour8522]

Luis! You are a fantastic teacher! Not everyone can explain complicated concepts in a way that every body understands. Your teaching style shows the depth of your knowledge! Thank you!

[@AmeyPanchpor02]

Really this is one of the best introductory video i have found. Knowledge + simple understanding examples = Gives very good understanding of topic.

[@kevdaag2523]

That was great the way you explained matric factorization and then turned it into an explanation of ML.

[@ws-ob4wy]

I find your teaching method not only to be great but also very valuable to motivate young people to take up Machine Learning. You could make it even better by also relating it to the math (Linear Algebra, Calculus, Probability) in a more familiar form. Make sure that anyone teaching and learning ML in a college environment will be aware of your videos. Great stuff.

[@SajjadZangiabadi]

The instructor does an excellent job of breaking down concepts and explaining them step by step in a way that is easy to understand. I appreciate the time and effort put into creating such an informative and well-presented video. Thank you for sharing your knowledge with us.

[@TheGenerationGapPodcast]

Most of us who have been watching your videos are changed forever. We are convinced now that there are better ways to teach machine learning and your way is one of the better ways. Thanks

[@VatsalaNundloll]

By far, one of the best videos on Matrix Factorization! I was looking for a good explanation on this and instantly clicked on this video as soon as I saw it was from Luis. Luis, you are a fantastic teacher!

[@SurajAdhikari]

Thanks Luis. This is one of the first videos I watched on Matrix Factorization and I understood them really well. Great job. Keep posting.

[@sarthaktiwari1889]

It is one thing to have a great hold over technical concepts and another thing to be able to explain them. You have both. Very well explained!!!

[@killuawang677]

This video deserve 10x more likes. I got to say it is so much better than Google's own recommendation system crash course...

[@hcordioli]

Luis, this is the best explanation I´ve ever seen, not only about Recommendation Systems, but basic concepts like Gradient Descent, Loss Function, Matrix Factorization, etc. Congartulations for your didatic way, and thanks for sharing !

[@glencheckisthename]

I searched about 20 videos and blogs, this is the best explanation about FM

[@UmeshRajSatyal]

Well explained and very easy to understand. Stopping here to thank you.

[@andis9076]

Man, YOU'RE GOOD ! I rarely see a video that explain things so clearly like yours !

[@amanzholdaribay9871]

WooooW! That has been as simple as possible! If person understands something, he can be able to explain it even to the child - I mean level of understanding is amazing! Thank you!

[@vinaysingh6664]

I really like the way you explained this concept in so simple words. At best what we do at deep levels comes from what we learn at the basics and clearing those is the most important thing, and I guess you took really good care of that. Again Thank you for this great resource. :)

[@spikeydude114]

You really did a great job of distilling what I saw as a complex topic to something practical and understandable. Great video!

[@MohitJaggi-f8h]

Nicely explained. Small nit: you say square to avoid ambiguity between positive or negative which is a misleading simplification. The reason to do that is to avoid the errors from canceling each other out when you add them up for all ratings. That is indeed the step you show next so easy to add an accurate explanation

[@crazywebhacker9769]

There are not many really good ML videos on YT. This is by far one of the best.

[@MmahamRroblox]

Tutor gave a clear understanding of matrix factorization. Also, even though this lecture was not about hyper parameters and gradient descent, but first time I got clear understanding of these two concepts.

[@fernandobezerra4040]

THE BEST VIDEO ABOUT MATRIX FACTORIZATION EVER! CONGRATULATIONS, TEACHER!

[@ultraviolenc3]

Great video! So much easier now to comprehend more complicated material after your explanation

[@nguyenhiep6639]

Thanks for your full explain inaction. It helps me really much to understand my project

[@chandanroy1789]

Great explanation! I was looking for something cool and simple to refresh my past learnings.

[@gobbledee55]

Wow... you did an outstanding job of explaining this topic. Thank you for this. It was very clear, concise, and the graphics were spot on and helped visually everything. Visual learners are all thankful for this presentation :D

[@sumitchhabra2419]

I haven't come across any video on internet with such an intuitive explanation. Loved it!!!

[@nikhilbelure]

elegantly explained. like the description very friendly introduction. I was struggling to see how matrix factorization plays role in recommendation system. no I got it Thanks

[@penguinmonk7661]

If you are areading this, you perhaps found the missing link in your ML knowledge, I sure as heck know I have, so don't pass up on it. Watch at least the first 15min. Praise: I am a CS Academic chair with a specilization in security and Distributed systems. Never have my peers in Machine learning/ AI truly explained to me why this works. I just knew it had to do with mathematics, and I knew how to use the software modules. I could implement them line by line and turn math found on wikipedia or textbooks into code. I have even been part of a research effort into self learning robots using hyperNEAT. From the bottom of my heart: THANK YOU. I finally understand how this works. Its been 3 weeks now and this entire fields has been opened up for me, I look at it with such different eyes and so much more appreciation and wonder. THANK YOU.

[@nnslife]

Great video! I rarely assign this kind of title to a video, but this was really great: easy and detailed at the same time! Once you put matrices like at 13:23, I was like: wow, this is how matrix multiplication should be introduced in colleges! Even many years later and with a good understanding of linear algebra, this adds so much intuition.

[@vishalmendekar7006]

One of the best video explaination i found till now. Everything is crystal clear with real examples. Thanks alot for posting the video

[@shelllu6888]

honestly the best video I've seen to explain matrix factorization. Thank you so much!

[@dayan5402]

Real-life application + theory in simple terms. Very nice! Thank you!

[@shamim-io]

Sir you are truly a great teacher. Such a beautiful presentation. U made the concept so simple. Very much grateful to you. Please keep making videos.. Love from india !!

[@黃煜棋-f3h]

It is so easy to understand such a difficult concept. You must be a great teacher. I like this kind of video very very much. Thanks a lot.

[@mkamp]

Absolutely wonderful. Thanks for taking the time to slow walk us through it.

[@premkumarpathare]

One of the best explanation about matrix factorisation. Once understand you can't forget.

[@jackshi7613]

Well explained concepts, really appreciate your nice video

[@ruchitchudasama1407]

This video blew my mind. I never imagined that the matrix multiplication that we learnt in high school could find such a huge application.

[@tejaswi1995]

Wow. Great content. Latent features concept got so clear after watching this!

[@phaniramsayapanen5890]

Great explanation, you seem to understand the concept very clearly. Subscribed immediately! any videos on expectation maximization, svd, dimensionality reduction ? or resources that you liked most ?

[@krishnaKumar-zi6ct]

Superb presentation! u have simplified and explained the concept so well...clear flow, great visuals. Thank you very much Luis!!

[@azurewang]

watched again after 3 years, still be amazed!

[@killuawang677]

Actually I do still have a question after this video: How do we know how many features we are supposed to have? i.e. how were you able to decide the factorized metrices are 2 x N and M x 2? Does it mean you might end up getting a feature that is a combination of multiple "actual features" and you need to further break it down?

[@samarthpianoposts8903]

That is something which people experiment with by seeing what gives them the best result. In general, people experiment with values proportional to the logarithm of number of unique items.

[@samarthpianoposts8903]

Since the video is over 30 min long, let me break it up 00:40 How do recommendations work (Netflix example) 07:35 How to figure out dependencies (Matrix Factorization) 16:03 Matrix Factorization Benefits 20:38 How to find the right factorization 26:35 Error Function for factorization 30:14 How to use the factors to predict ratings (Inference) Really informative and comprehensible. I was wondering what is the difference between collaborative filtering and the Deep Learning recommendation algorithms. Now I understand that DL is one of the ways to perform the factorization for the collaborative filtering method.

[@SerranoAcademy]

Thank you, that's very helpful! Added the timings to the video.

[@guanyanlin1933]

No doubt. It is definitely an excellent tutorial, and give a reasonable answer of why the weights in the hidden layer is the embedding of a movie or a person. Thanks a lot.

[@karannchew2534]

Hi Serrano, A suggestion please. Before walking through a detailed example, please first introduce the overall concept/algorithm/intuition, and, the content/agenda. First tell the learner what they would expect to see/learn, then start teaching them. Thanks for all the useful videos!

[@mingman753]

OMG this is wonderful. My mother tongue is not English but this lecture is much better-understanding than others in my language. I logged in to 'like' this video. Thank you so much for your video!

[@raphaeldayan]

PERFECT VIDEO! YOU ARE THE BEST! So easy to follow, so clear, thank you

[@sidagarwal43]

Very clear and lucid explanation. Thanks

[@nguyenkimtrang9525]

30 minutes gold ~ the best explaination ever! Respect! Many thanks to you!

[@kamalamarepalli1165]

What a visual treat to understand the logic and concept behind....soo good and very well explained.

[@vulkanosaure]

Thanks so much, it's extremely well explained, better than other things I saw on this topic. The stucture of the NN that solves this is clear in my mind noW

[@Josh-di2ig]

By far the best ML teacher ever. Thanks for a great vid!

[@armasaaf6180]

thank you for making it easy to understand. Great job!

[@jonlenescastro1662]

Definitively the best explanation on YT

[@josephhsueh6456]

appreciate your efforts to make such a good video! thank you! everything is detailed! love it

[@renemartinez3014]

Excellent video. A little bit slow pace but thanks to it there´s little space for doubts or misunderstanding. Great job.

[@blackstallion9605]

This is amazing, it has really opened my mind. Thank you so much

[@hasush]

2 hours spent trying to understand MF factorization and SVD++ in Wikipedia to no avail ... 30 minutes here and super clear... awesome thank u! One question, how to deal with new users, movies, and ratings? Retraining? What other solution to avoid retraining?

[@title601a]

Awesome!!! Nice presentation, simple and easy to understand. Thank you so much :)

[@derekhe6816]

Thank you so much for this video. Your teaching style is great and you presented all the information comprehensively but simply that I feel like I have a much greater grasp of the concepts. Here are 2 suggestions: perhaps invest in a microphone that gives you clearer sound, because currently I have to turn the headset way up and the levels are too high that it can kind of hurt. Also, if you could spend more time at the end writing out general formulae of the algorithm like Andrew Ng, that would be nice. Once again, thank you so much for this video!

[@giangpham6044]

Well explained and very easy to understand. Thanks you

[@guitar300k]

in your example, we have two latent factors, so how do we know which one should increase which one should decrease to reduce the error, it seem like you have to increase/decrease both of them at same time

[@nikhithasagarreddy]

Super sirr, every class is very clear ,, there are only few classes available. Please upload every class sir,,😘😘😘

[@mohitaggarwal6220]

The explanation for gradient descent was great, but I'm a little confused about the 25:00 minute part. In the matrix, the (1,1) element is 1.44, but the actual value is 3. So, we need to increase something. It could be [f1][m1], [f2][m1], [A][f1], or [B][f2]. How do we decide which one to increase? And by increasing which value and by what factor can we get accurate results? Increasing a single value or multiple values can potentially bring us closer to the answer. If anyone has an answer for this doubt, please clarify. I'm curious to know.

[@ChetanRawattunein]

Thank you KZbin recommender for the video🤗. I was really looking for something this informative.

[@nurkleblurker2482]

Great video. But how do you determine a users preferences for movies in the first place?

[@sajadkarim]

Many thanks for the video. It was really helpful and the way you explained the concept is outstanding. 101/100!

[@abhishekrupakula1613]

Thank you so much Luis. Very well explained.

[@ZavierBanerjea]

As always a big fan of Luis! He is a master of "Explain this concept to a kid" Idea. Of course, that is what Greatness is!

[@codingpineappl3480]

Best video, you can find about matrix factorization. Thanks a lot

[@aakashyadav1589]

are we giving features of users and movies as input or are they extracted by MF algo. itself? During gradient descent, is the algo. learning weights to each of the features or the algo changes the features, as shown in the video?

[@dhruvbarot]

Awsome , simple , mindblowing .... both explanation and presentation

[@in-my-opinion6423]

Awesome. Nowhere would one see such a clear explanation

[@scherwinn]

Excellent way to show Gradient Descent and error function.

[@ASHISHDHIMAN1610]

Hey sorry for knit-picking but at 17:01, the red triangle would have transposed shape i.e. greater height(2000 users) than width (1000 movies) !! Great video though !! Please make one on Gaussian Mixture models.

[@shahnawazhussain7506]

WOW. How simply explain it. Great Video.

[@Lae56]

Awesome! Truly appreciate. Very informative and easy to follow.

[@iamdanielkip]

I was driven here after reading a chapter on RGA's book where the mention "collaborative filtering". I was curious and decided to learn more about it. I would like to know though, what computer language is generally used to achieve this? Thank you for the very simple and fun explanation.

[@alirezariazi5325]

complex information explained very simple, TNX!

[@amandaahringer7466]

Excellent explanation, great job! Thank you for sharing!

[@shaikhmosakib604]

What A Explanation Dude, Thank You So Much

[@msnjulabs]

How did we figure out what columns should be in the Factorized matrices???. Also how did we figure out how many factors should be in the resultant matrices?? Also how did we ident those columns? Say columns to be comedy and action for user?

[@SerranoAcademy]

Great question! The model does it automatically in the training process. In this example I created two columns for exposition, but in reality, sometimes they mean something (features), but sometimes they're combinations of features, or things that we can't identify, but that the model picks up.

[@sargun.nagpal]

Given a new movie M6, how do we assign feature values for the movie? Related question is- given a new person E, how do we come up with their interest in different features?

[@TitasSaha-er5ye]

You are the best !! I am so amazed that i understood the video in just one go, Thank you :D

[@DevanshiRuparel]

Do you think the dot product calculation assumes that a low rating (of 1 on 5) will not decrease their overall rating for the movie? (I.e. the calculation only adds the preferences & if Person A doesn't like Action, their rating for a comedy + action movie will equal that of only a comedy movie and the overall rating for a movie will not be reduced because they dislike action). Is this a limitation of the dot product calculation? How do you think Netflix takes this into account?

[@Betterdailyy]

Thank you so much for this! It really helped me!

[@youngzproduction7498]

I must say thanks for your effort. This vid literally saves my day.

[@TejasPatil-fz6bo]

This video made my day...thanks Prof. Luis

[@rosabasagoitiastigarraga8673]

Very nice example and a very good explanation. I was trying to reproduce it using recommenderlab but still some details to fix!.

[@NidaSyeda]

This excellent! Thank you for simplifying it. You are incredibly talented.

[@dpdp006]

Thank you for your efforts on details Why is teaching not made as simple as you just explained.

[@rigoluna1491]

By far the easiest thing to follow, thanks

[@Azureandfabricmastery]

Thanks. Nicely explained with visuals to understand matrix factorization.