Matrix Factorization in Recommendation Systems | Netflix Recommend Movie

[@Serialloserr]

Your smile while illustrating very complex idea is really really reliefing

[@Mars.2024]

Thank you for your simple and effective way of teaching . I'm a beginner but now i have a better view of recommandar system .

[@manoharnookala5341]

Thank you so much sir, i got full clarity on matrix factorization. and your explanation is very easy to understand

[@binodsuman]

Thank you for nice words. Keep learning !!

[@RohanDreamerz]

Good explanation. Thank you! Please do a full end-to-end recommender system model with python using this technique.

[@ghousiafatimauxd2418]

At 9:24 how did you get 15 elements? please elaborate

[@ganeshsubramanian6217]

I think he assumed he had 5 columns instead of 4

[@Tabaraei]

Brilliant explanation! Keep going ..

[@manideepgupta2433]

Sir, You're Amazing!! Thanks for the explanation!

[@sachinkun21]

Hey Binod. Loved it. Keep up the great work.

[@antsathemathteacher]

I really like the way you explained it... Good job. Thank you

[@msktheabbasi4818]

thank u sir👍

[@vishavgupta3717]

you are awesome sir. one of the best explanation

[@maralazizi]

Thank you so much! it was very helpful!

[@swadeshification]

Great Explanation Sir !!

[@binodsuman]

Hi Swadesh, Thank you for nice words and suggestion. Glad to know that this Matrix Factorization Recommendation system video helped you to learn. Keep Learning !!

[@sakahamuru]

at 9:23 , you say we have 15 elements but actually it's 12 which is smaller than the total of the factorized matrices (which is 14). How is this better? or am I missing something?

[@pathfinder9547]

bhai answer mila iska? mujhe bhi yahi doubt hai.

[@sakahamuru]

@@pathfinder9547 For much larger sets of data, for example 1000 users and 1000 items being rated, UserXItem ratings matrix would be of size 1000000. But if you do matrix factorization with ~30 latent features you could get 1000*30 + 1000*30 = 60000 size matrix which would greatly save space to represent essentially the same matrix. I think in the video because it is a toy example this logic was not clear.

[@pathfinder9547]

@@sakahamuru These 30 latent features should be linearly independent,right? The rest 70 would be a function of a few of these features ?

[@sakahamuru]

@@pathfinder9547 im not sure what '70' you're talking about. But the 1000*30 matrix of users*features and 30*1000 matrix of features*items will matrix multiply to represent the full 1000*1000 matrix, so very little information is lost in matrix factorization via gradient descent. The 'factor' matrixes map the complex relationships between users and items via these 30 generated features. The '30' number can be increased or decreased depending on the complexity of the relationships. A more complex relationship will have more latent features, and simpler could have very few latent features. The number of latent features is a hyperparameter which can be optimized for finding the best sized factor matrices.

[@mdgazuruddin214]

In 11:23 , he Clear elemnet matter where right side have less element han left side.

[@muaadh_abdoalsabri1941]

wow, amazing explanation thank you so much, Sir.

[@shuyaebkhan1603]

Are 15 elements kaha se aaya ?

[@vinnakotasaranchaitanya5461]

Nice explanation sir👍

[@binodsuman]

Glad to hear Vinnakota, this Matrix Factorization video helped you. Keep Learning and thank you for your nice words !!

[@arnabmaity7505]

Thank you sir. I love the way you explain

[@keerthip3521]

super sir

[@thechhavibansal]

thanks sir

[@binodsuman]

You are welcome Chhavi. Good to know, this Matrix Factorization Tutorial helped you. This is one of the my favourite videos, I had put a lots of effort to add many more concept in one video. Keep Learning !!

[@122arvind]

where is next code video not getting

[@manasapachavanitap393]

Super sir..

[@Abhi-qf7np]

Nice explanation.

[@seongchulkwon1762]

greate

[@manashi_100]

thank you

[@believer-n3t]

video was not looking worthy until i watch this till 15:56

[@rajanchoudhary1912]

great job done!!! do with python code

[@binodsuman]

Thank you Rajan for nice words and suggestion. Will try to upload one video with Python code and really happy to know that this Matrix Factorization Recommendation system video helped you to learn. Keep Learning !!

[@abraramirhussain6778]

Good explanation sir!

[@binodsuman]

Happy to hear Abrar Amir, this Matrix Factorization videos Tutorial helped you. Keep Learning !! @binodsumanacademy

[@FlexingD999]

tqvrm