How to Design and Build a Recommendation System Pipeline in Python (Jill Cates)

It's so beautiful how you include those content in merely 20 mins! Well explained!

Great talk for a general overview on recommendation systems! From there I could deepen in the subjects I found interesting or didn't know about, in my opinion it's a great video for people with a general knowledge of ML or maybe that have some knowledge in other applications but never touched Recommendation Systems.

Just one thing that doesn't come clear to me at the pre-processing part:
When she talks about normalization, she talks about applying mean normalization for the users ratings, which comes clear, but the slides show a formula with "user-item rating bias" which she skips explaining, can someone explain me on where does the formula come from and if it's something that you should need to subtract from every cell? The fact that there is a variable for "global average" and another for "item's average rating" kinda confuses me, does the global average regards the whole dataset of movies? Thanks!

Suddenly matrix factorisation comes up. Why? What are its benefits and limitations. Ok i never studied this but it looks to me that im very dumb or the speaker jumps over a lot of issues.

How do u make predictions bcz in knn for predictions we need train or test data by splitting but here we r using different approach for this so how gonna we make predictions for ds?

amazing amount of content in just 20 minutes! Also, thanks for covering train/test split- not everyone covers that with collaborative filtering.

Any software I can use instead of building my own?

Nice presentation. In recommendation system, how do you define the relevancy for model evaluation hyperparameter tuning? Furthermore, how can you do this offline more accurately?

Hmm.. Jill Cates, sounds very much like Bill Gates.

Nice talk !

very crisp but makes the point.. thanks

Very nice and smooth introduction .. Thank you .. I hoped for a python code implementation as well

How much u charge for making a video recommendation system for Android app?

How to deploy the model in a cloud platform and then consume in front end app like react. Thanks

Thank you so much for short and very informtive lecture. It helped me a lot to start my project on recommender system. :)

There is nothing new to learn from this presentation. Don’t waste time!

a good one, thanks

Excellent presentation. Thanks

Very clear. Thanks

what is a good value for sparsity

Excellent! Congratulation for your presentation!

Excellent talk on Recommender System

You have made the different concepts really clear. Thank you.

how do you update your k-latent factor matrix after a new user arrived? do you have to re-multiply the whole user-item matrix again?