Home page

How does Netflix recommend movies? Matrix Factorization

Frames How does Netflix recommend movies? Matrix Factorization

Description How does Netflix recommend movies? Matrix Factorization

Comments How does Netflix recommend movies? Matrix Factorization

wow, the way you explain this topic makes it so easy to understand Comment from : @hiimuc

Very nicely explained! Keep the good work going!! Comment from : @prathameshdinkar2966

It is so fun learning concepts from your videos, it doesn't feel like studying and not at all hard to remember after learning this waybrI wish you had video on all concepts especially LLMs, AI Agents etc Comment from : @saidisha6199

Nice! Comment from : @bvlgvkov

woooow, i am really grateful for this basic explanation that makes me understand this complex concept especially when explained so theorically Thanks a lot you're the GOAT Comment from : @OthmanIBRAHIMI-y6c

Awesome , very well explained Comment from : @SaiPurnimaSunku

bro what a great explanation Comment from : @kamal_douma

I’ve heard the saying, 'You don’t really understand something unless you can explain it to your grandmother' Watching this, I can totally relate this Amazing explanation ! Comment from : @AG-dt7we

Thanks for explaining it so easy to understand! I love you! ❤ Comment from : @AILearnings-s3t

Great explanation! Thank you! Comment from : @terryliu3635

thank you Comment from : @channadissanayaka6450

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 Comment from : @MohitJaggi-f8h

Many thanks to you Comment from : @ETeHong

Awesome video 🙏🏼 Comment from : @yoalihuerta5966

Netflix's movie recommendation system is so fascinating I've always wondered how they seem to know exactly what I want to watch next Matrix Factorization sounds like some seriously advanced AI magic Speaking of AI, I've been reading about how platforms like SmythOS are making it easier for companies to build their own Comment from : @Abhinayanagarajan-x8p

Awesome video man Great teaching method Reminded me of 3blue1brown Comment from : @dhananjay1481

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! Comment from : @ZavierBanerjea

Do you take the blank cell in the sparse matrix as zero to calculate its factors? Comment from : @rishabhchoudhary0

amazingly taught thank you so much! Comment from : @just_a_viewer5

how do you choose the number of features in the 2 matrices, ie how did you choose to have 2 features only? Comment from : @bendim94

WOW How simply explain it Great Video Comment from : @shahnawazhussain7506

Thank you! Comment from : @sriks4003

Great explanation Is this factorization a Non-negative matrix factorization? Comment from : @serafeiml1041

mind blowing!! Comment from : @CharanSaiAnnam

You are the man! Comment from : @asifadil5529

Amazing video, thank you so much Comment from : @niattesfay6086

Instead of finding relation between different users we can find relationship between a user and the features of the target variable Comment from : @Utkarsh_vns

very well explained! thx! Comment from : @topcan5

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 Comment from : @gobbledee55

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! Comment from : @Vatn7

Hi prof Thank you for an amazing lecture, but can you tell me how can i deal with cold start problems like if the user is new and don’t have any info or the movie is new? Comment from : @thanhthanhtungnguyen8536

Nicely explained and easy to grasp !!! Comment from : @mohamedarshad-l7u

The video just summarised graphs and matrices can be viewed as isomorphic systems in machine learning!brHow many of u feel so?👇 Comment from : @psychopedia1631

i thought you were going to show actual factorization methods like QR or LU Comment from : @boywithacoin

Excellent Sir !!!! Comment from : @tushar7305

Awesome video man! Comment from : @danamirafzal9425

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? Comment from : @msnjulabs

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 144, 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 Comment from : @mohitaggarwal6220

Beautiful Comment from : @oyadoyevictor1526

whats the name of training model sir 27:30 Comment from : @dzearilife-darija

Pretty cool movie, thank you Comment from : @wiktorm9858

Man, YOU'RE GOOD ! I rarely see a video that explain things so clearly like yours ! Comment from : @andis9076

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 Comment from : @SajjadZangiabadi

One of the best explanation about matrix factorisation Once understand you can't forget Comment from : @premkumarpathare

My name is also Luis, but I pronounce it LooEEs, and it sounds like you say it as lwis Never thought I'd see a Luis pronounce it like that Comment from : @XxAssassinYouXx

Thank you so much for this! It really helped me! Comment from : @Betterdailyy

thank you for making it easy to understand Great job! Comment from : @armasaaf6180

ChatGPT recommended me your video for PCA explanation and now I basically owe my ML knowledge to you Amazing stuff! Comment from : @psc698

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? Comment from : @aakashyadav1589

Fantastic Comment from : @snowwolf4148

I searched about 20 videos and blogs, this is the best explanation about FM Comment from : @glencheckisthename

This was so good Comment from : @pratikmandlecha6672

you do great Comment from : @muhalbarahusainhaqb5737

Amazing explanation! Thank you Luis Comment from : @jjj78ean

Thinks a lot! It's really useful and interesting Comment from : @mariannelaffoon2598

Wow Great content Latent features concept got so clear after watching this! Comment from : @tejaswi1995

So informative and easy to follow I love this Thank bryou 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 Comment from : @mhmd300044

could you share the slide? Comment from : @tuvo6927

You are a great teacher Thanks Comment from : @UmairMateenKhan

Ok wow, that was amazing Comment from : @gagandeepgopalaiah3643

Amazing explanation Totally worth watching Comment from : @ishandvd

Just amazing Comment from : @MDSakib-hz1kh

You really did a great job of distilling what I saw as a complex topic to something practical and understandable Great video! Comment from : @spikeydude114

Great video, you broke down ML into easy-to-understand terms Great job! Comment from : @bradhammond5581

watched again after 3 years, still be amazed! Comment from : @azurewang

Thanks a lot for such a user-friendly video!!! Bravo! Comment from : @ttmhui

You are the best !! I am so amazed that i understood the video in just one go, Thank you :D Comment from : @TitasSaha-er5ye

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 Comment from : @huynh75

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! Comment from : @shivanshkaushik383

Well explained, saved me a lot of time Comment from : @mactavish9906

This is amazing, it has really opened my mindbrThank you so much Comment from : @blackstallion9605

I think you got Sharknado wrong, it should 3 in comedy and 1 in action Comment from : @Yan-dh5yc

Thanks a lot, really good course Comment from : @ewenbernard684

By far the best ML teacher ever Thanks for a great vid! Comment from : @Josh-di2ig

Thank you so much Sir, this video is from 2018, yet it is still helpful I've been studying this lately for our Defense and it's very informative Thank you and Godbless Sir <3 Comment from : @madara9897

teacher you are a legend , thank you so much Comment from : @osmanovitch7710

This is such a great video! Just noe question is that when training the data, you don't necessarily have everyone rating all the movies, how do you get the values of the factorized matrices correctly? Thanks Comment from : @zhangpeng932

Best video, you can find about matrix factorization Thanks a lot Comment from : @codingpineappl3480

Great video with excellent explanations! Can you provide the link to the video about gradient descent as mentioned around here 29:20? Comment from : @sarius7

Excellent video Comment from : @iamamarnath2499

Thank you for your efforts on detailsbrbrWhy is teaching not made as simple as you just explained Comment from : @dpdp006

Excellent explanation, great job! Thank you for sharing! Comment from : @amandaahringer7466

Very good explanation Comment from : @willdog4352

Excellent Comment from : @sadrahakim

Very nicely explained in detail🥳🥳🥳🥳🥳🥳🥳🎈🎈🎈🎉🎉 Comment from : @payalsagar1808

wowthank you so much for this explanation ! Comment from : @AbeReplyToKar

Very good explanation 👏👏, but how we evaluate performance and accuracy of this model against other models? Comment from : @gauravmodi12

Love it! Thank you, very very well explained:) Comment from : @MalteResearch

Thank you so much!!! Comment from : @shulundong827

Well explained concepts, really appreciate your nice video Comment from : @jackshi7613

Pure Gold as usual Comment from : @anasal-abood9649

Great video Luis :) I have one question though that how do we decide the number of latent of latent features and what are the trades off using high/low number of latent features Thanks Comment from : @usmanabbas7

Fantastic Comment from : @kamogelothokwane8312

Since the video is over 30 min long, let me break it upbr00:40 How do recommendations work (Netflix example)br07:35 How to figure out dependencies (Matrix Factorization)br16:03 Matrix Factorization Benefitsbr20:38 How to find the right factorization br26:35 Error Function for factorizationbr30:14 How to use the factors to predict ratings (Inference)brbrReally 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 Comment from : @samarthpianoposts8903

Love the video! Comment from : @SonLe-mk4sq

Related How does Netflix recommend movies? Matrix Factorization videos

Top 10 Highest Grossing Movies 2024 | Highest Grossing Indian Movies #shorts #movies #top10 РѕС‚ : MoviezTale Download Full Episodes | The Most Watched videos of all time

Dictionary Learning for Massive Matrix Factorization - RecsysFR РѕС‚ : RecsysFR Download Full Episodes | The Most Watched videos of all time

[Intuitive Deep Learning] 1.5 Spanning sets u0026 matrix factorization for data representation | PCA РѕС‚ : BASIRA Lab Download Full Episodes | The Most Watched videos of all time

CS8850: Matrix Factorization РѕС‚ : Sergey Plis Download Full Episodes | The Most Watched videos of all time

[CPSC 340] Sparse Matrix Factorization РѕС‚ : Mike Gelbart Download Full Episodes | The Most Watched videos of all time

Online nonnegative matrix factorization and applications - Deanna Needell - FFT Mar. 28th, 2022 РѕС‚ : Norbert Wiener Center Download Full Episodes | The Most Watched videos of all time

Robust Non Linear Matrix Factorization for Dictionary Learning, Denoising, and Clustering РѕС‚ : IFox Projects Download Full Episodes | The Most Watched videos of all time

Exploring Matrix Factorization with Python: A Step-by-Step Tutorial РѕС‚ : Data Science Center Download Full Episodes | The Most Watched videos of all time

Binary Matrix Factorization via Dictionary Learning РѕС‚ : XOOM PROJECTS Download Full Episodes | The Most Watched videos of all time

Matrix Factorization: Latent Features u0026 Embeddings - M5S41 [2019-11-15] РѕС‚ : Victor Geislinger Download Full Episodes | The Most Watched videos of all time