Recommender Systems

This is CS50

[@daniel10263]

A very fascinating lesson in how the recommendation system of Netflix, Facebook works, etc. Thank you so much CS50 staff!

[@harlongbitimung4108]

Great video. I appreciate how you have explained the concept in a much comprehensible way.

[@alaaalqhtani6131]

Thank youuuu soon much! Such simple and detailed explanation, you can't imagine how much this helped me! It gives me the idea of 'recommendation' and their matrices Thanks again ..

[@tharangasaitm]

Thanks for the simple explanation.

[@marshalldteach1109]

I thought your Linuz Torvalds, but thanks, great explanation!

[@wow404notfound4]

same, haha, I came across thinking it was Linus talking

[@rahul_bali]

You are!?

[@sasikanth1329]

Ya exactly..... 😀

[@etienneekpo348]

quite true ... thought it was him!!

[@ogskun]

maybe he's a fan..or he's an impersonator of Linus hehehe

[@ArenMarkB]

Amazing lecture. Thank you.

[@Tori_V_]

Great video! Very explanatory and easy to understand!

[@kristhianortiz151]

this was 8 years ago, amazing

[@sanskrutidarpan9107]

Thank You.Explaination is so simple so anybody can easily understand.

[@nsambataufeeq1748]

When i audited this course 7 years ago, i had no idea i will do ML for a living.

[@mehdijb752]

thanks for your great and simple explanations

[@lukmanoyee3731]

WHAT A SIMPLE AND PRECISE EXPLANATION....... thank YOU SIR

[@YEESJAY]

ITS SOOOOOOOOOOOOOOOO HARD

[@madhur089]

good job...thanks for explaining in simple way :)

[@MrHiwa1988]

thanks for you, and for using simple example for understanding this topic

[@rickyharewood]

Brilliant explanation!

[@johng5295]

Thanks in a million. Awesome. Where have you been all these years.

[@georgekokonya8892]

Thank you. It's very simple and easy to understand

[@maryammehboob8285]

Thanks for simple and brief explanation.

[@iftekharjoy7118]

Just awesome!

[@annisas.a3580]

omg I just saw this video on 2020 thanks, very clear explanation!

[@waseemanwar3327]

A fantastic way of explaining the content. I understood the underlying concept. Thank You so much.

[@YEESJAY]

Your not welcome :(

[@luanzambinatimanara2529]

Great video! Congrats

[@s.e.7268]

omg, it is an amazing lecture!

[@sanyakapur6536]

Great explanation!

[@prashantchavan789]

very excellent talk.

[@jm7124]

Thanks for sharing this video.

[@G47_Code]

진짜 굉장한 강의들이다 정말 하버드 진짜 만세다 ㅠㅠ

[@oussamaoussama6364]

Excellent explanation! Shukran.

[@YEESJAY]

EYVALLAH BEY

[@palermodpr]

Thank you a lot!

[@hanggianggono3765]

nicely done, i will implement content based since it is more reasonable in early

[@nitroflap]

Linus Torvalds explaining us recommendation systems... interesting

[@stelakoul]

Scaz for president!!!

[@skandarbenali3695]

clear explanation. thank's

[@ShreyaBhandare]

amazing, thanks

[@user-or7ji5hv8y]

Great presentation.

[@oskrm]

nice videos

[@user-go2yu4hq5p]

thanks a million for you

[@Lamoboos223]

thanks, you helped me in my GP

[@1tridibdey]

Can I use the matrix factorization technique for recommendation where the scenario is like I have 972 unique user and 3810 unique items and 24 unique country id. Like in a common movie recommendation ratings are there and we predict ratings and then show recommendation system. In my case I have country id instead of ratings. is it fundamentally incorrect or I can go ahead with this?

[@manujas1777]

Thanks sir good work👍

[@sachithadilshan9191]

Thanks Really helpful

[@adrianfathursetyawan9247]

thanks for your explanation about the remcomendation 's system sir

[@ehsanamidi4619]

thanks,for video

[@muwaffaqimam3790]

Thank you .

[@shyland20]

i wold like to know what is the filed for "software engineer" to study in order to control this filed and specializes in this field ? thanks (i'm looking to hire someone at my location)

[@harshalipatil8425]

Thank you sir.

[@chinmayjoshi9114]

How do you determine the value of k in SVD? Is stochastic gradient descent used for that or is it a completely different method?

[@MaoRuiqi]

Thank the Math Gods that sent you to me!

[@rkgrap]

Great explanation sir

[@safwankhan199]

THIS man is an og explainer lol

[@xhole]

it seems like an efficient tag system is more important than recommender algorithm

[@hiwotgirma6234]

thank u so much

[@safrizal513]

thank you

[@deepgsingh]

very nice explanation

[@CodeXBro]

Thank you

[@user-lv7kw1yj6j]

Thanks for your lectures. Is it code Python?

[@G47_Code]

amazing lesson. thank you professor

[@rumbl3d]

For a moment I thought he was Linus Torvalds

[@vovantanat9615]

Yo after those years this lecture is so good 😱😱

[@rikhsan]

it helps. thanks

[@piotr780]

Where is the rest of this course ? I dont see the playlist

[@asutoshmittapalli]

Didn't know Linus Torvalds taught CS50

[@YEESJAY]

IF YOU DIDNT KNOW THAT THEN YOU CANT BE IN CS50 I AM IN THEIR UNIVERSITY FREE SCHOLORSHIP

[@agilasadi5044]

Great examples, but I really wonder. Doesn't this kind of peration take too long to run? I mean running this kind of algorithm would probably take 30ms, but considering you might have thousends of users trying to run the algorithm simultaneously, it might be a pain in the ass. Aren't there better ways to deal with it?

[@YuzuruA]

Unfortunately Netflix seem to have throw all that work away

[@SayantanTalukder]

Well what about the Google search engine? What kind of recommender system do they use? How do they decide the precedence of the search results for an user? Do they use content based filtering based on the search keywords, or does they use a hybrid recommender system where they first collect sites from the internet based on the search terms and then use collaborative filtering based upon all the users who have searched using the specific terms and then predict which link the user is most likely to click on based on what others have clicked on?

[@lechat5717]

This is an another topic, called Information Retrieval

[@dushyantsharma4912]

Google uses Page Rank algorithm for listing the search results and I think that they use hybrid system for recommending similar searches.

[@nguyenhuyphat6020]

Hi sir, can you please give me a lecture on Building Recommendation System ?

[@jamesrusso4220]

lol the CC spells boolean as "bullion"

[@naatworld.]

Thanks Sir

[@gaurangsolanki2542]

you look like young Sheldon's father

[@KevinEontrainer381]

> Want to learn about recommender system > Sees Bechdel Test > Pauses Video > Search "Why is Bechdel Test even necessary?" For 30 minutes

[@bicbicw5660]

Kevin Eontrainer lol

[@tubex1300]

Hi Sir Will you provide the sample code of the video about? Thanks

[@lochanathambeliyagoda5065]

Lol i saw this on my reccomandation

[@KayathriDevprasad]

is it possible to apply recommender systems to Intrusion Detection Algorithms?

[@YEESJAY]

no its not possible

[@yashsvidixit7169]

anything is possible if you are brave enough.

[@mahmudhossainshanto7227]

:D

[@rabnawazbhanbhrobhanbhro8509]

what is the cross domain recommendations please explain it

[@rabnawazbhanbhrobhanbhro8509]

please give me some idea about cros domain recommendations

[@ErikPontifexAudio]

There must be a better way to do this...I don't identify with most of what the "big sites" recommend me. KZbin, for example, keeps recommending me chess related videos when I've never watched anything remotely chess related as far as I know. I guess this must mean that my tastes are somehow adjacent to the subject of chess, but it sounds like the system would yield better results overall if it could somehow realize that I am not responding to that particular subject.

[@wnwillyndirangu]

+Erik Pontifex your concern is genuine but another aspect of machine learning (which is basically a field of AI) is to try and predict things you might like just like netflix . one way of doing this is grouping people (clustering ) with similar tastes together . This might be what youtube does and it just might happen that the cluster you are in consists of people who like chess thus the recommendations.

[@abhishekkhanchi9105]

Nice

[@hamlinhamlinmcgill630]

Next week i have my exam based on recommender systems, hope i will pass...

[@jackvu.hustle]

Jack Vu is here.

[@YEESJAY]

i came from clevered.com pre class video

[@YEESJAY]

Really nice i also came

[@YEESJAY]

@zeyrox you gotta be kidding me nah?

[@YEESJAY]

no i am not lol

[@YEESJAY]

ohhh ok KOKO MO MUJEH BI DO

[@YEESJAY]

SAME BOI