How Netflix Predicts | Recommender Systems

How do Netflix, YouTube, and other platforms predict what you'll watch next? Dive into the fascinating world of recommender systems (Content Filtering vs Collaborative Filtering) and the mathematics behind your personalized recommendations.

In this video, we explore:
- Content Filtering vs Collaborative Filtering
- The Netflix Prize Problem
- Matrix Factorization explained simply
- How patterns in user behavior predict preferences
- Real-world applications beyond movies

Perfect for students and professionals interested in:
- Machine Learning
- Data Science
- AI Systems
- Software Engineering
- Recommendation Algorithms

Original Research Paper:
"Matrix Factorization Techniques for Recommender Systems"

Key Timestamps:
0:00 The Netflix Prize Problem
2:06 Content Filtering Explained
4:36 Collaborative Filtering Approach
5:26 Matrix Factorization

#MachineLearning

Paper in this video:
Matrix Factorization Techniques for Recommender Systems

[TheKmisra]

I think it should be noted that for the cold start problem, you'd want to use content filtering to define which users to show those new items to - hence, a combination of content and collaborative filtering is the best approach.

[whuzzzup]

And then there is Amazon asking me to buy a second washing machine.

[pritamdas]

These are some solid gold videos on your channel you are putting up for free! Your incredible knowledge, such hardwork and the will to put such amazing educational concepts before the audience is really creating these masterpieces! Absolutely love it! 💗

[markheaney]

I don't understand why this channel isn't more popular. From the beginning it's been great.

[ernietam]

Thanks a lot. It is so simple that I can understand immediately.

[snowwolf]

Dude, literally watched a zillion videos on YT, nothing comes close to this video. The SVD simplification is on another level!

[roadmonitoroz]

Interesting video. I downloaded my netflix data once. It is amazing how much data they actually collect . One of the bits they collect is how long you watch each video (whether the actual movie) or the preview clip on the movie selection screen. i.e. If you watch the whole thing, you are somewhat interested in it and "that type of movie".
It also logs what suggestions it gave to you and why that suggestion was given (due to another video) .
It also collects search terms (full / partial) and what results were given to you. i.e. You type "term" and up comes "Terminator 1, 2, 3", "The terminal" (totally different type of movie)

[holyflame]

The name I learnt this as in Uni was Singular Value Decomposition. Same thing, different names. Great video as usual!

[ryanmckenzie]

I love all the artistic choices you guys make when putting these videos together, they have a spacious mood to them. It’s a little sad to read other viewers don’t like the music choice as much, each to their own I guess.

[KenCubed]

I am a mathematics phd student doing my thesis on low-rank matrix completion, it was great seeing this video show up in my feed! One of my biggest concerns was why we can assume that real life data is part of a low-rank matrix. Even though data being non-random and part of a low-dimensional space is a very reasonable assumption, the issue is that the space of low rank matrices is a very specific low-dimensional space, so why should we assume that our data lies on this specific low dimensional space? The features argument seems fair to me as why it may be reasonable to assume that our data is low-rank.

[NeuroPulse]

Art of the Problem is one of the better things on the internet.

[patricksweet]

My goodness this is such a great video. Just now diving into your channel and loving what you're publishing. Thank you! Just subscribed.

[arjunbansal]

I am loving every piece of content.. The explanation are best i have seen before.. I hope this channel gets more attention

[vaiterius]

I’m attempting to make a video game recommendation system from a Steam games dataset and your video was super helpful to me!

[orjihvy]

Really like how the explanation is concise and clear.

[syedistiukraja]

One of the coolest algorithm that is taking 1/3 of my day in scrolltime.

[jasertio]

Very interesting!! I would love to see more videos on this topic. I would guess that the amount of features can be increased in order to have a more accurate result, at the expense of greater computing power and storage requirements.

[christianalexandernonis]

The bg music feels like being in an horror movie lol

But the video is great

[JustSkillGG]

I AM SO HAPPY i discovered this channel!!!

[刘新路-zn]

The real problem here is traditional recommendation algorithm would recommend to you with things you already have. We need a new algorithm which can analyze and tell you what you may need to get in future, based on historical data.