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!
Incredible presentation! I semi-disagree with precision and recall being good evaluation metrics for a recommendation system using a masking technique to evaluate model performance during the offline training phase. This is due to them demanding the output of the model to be binary, where as masked-prediction in this case would represent more of a regression problem leading RMSE to be a more valuable evaluation technique. Great presentation though, very clear explanations.
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?
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?
I think we just need to optimize for new users only. stats.stackexchange.com/questions/320962/matrix-factorization-in-recommender-systems-adding-a-new-user
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.
nothing new for whom? Maybe not for you, but maybe then you are not the target audience. Then just don't bother to watch the Video and take your business elsewhere
There are better videos out there who teach the same so don’t waste your time on this video. As simple as that. AutoML will kill data science jobs so I decided to move on to another technology. Personally , I would suggest learn blockchain because it’s plays a huge role in security and also ensuring any data exist inside a blockchain is tamper proof. ML + Blockchain = 💯💸🤑
@@joeljoseph26 you should not waste your time writing this stuff, which has nothing to do with this video and others shouldn't waste their time reading it. Don't you waste anymore of other peoples time :)
what an arrogant rude person. Nothing new to you does not mean that no one else would find this useful. This is a very good helicopter view for someone who is new to build recommender system using collaborative filterings. If you REALLY value your time that much, you should probably not write this time-wasting comment. So yeah, you are just bitter and not happy with your own life, and leasing out on an innocent target.
It's so beautiful how you include those content in merely 20 mins! Well explained!
I’ve watched many recommendation engine videos and this is by far the best I’ve seen! Fantastic expertise and thought leadership.
amazing amount of content in just 20 minutes! Also, thanks for covering train/test split- not everyone covers that with collaborative filtering.
You have made the different concepts really clear. Thank you.
Concise and to the point, Thank you
Thank you so much for short and very informtive lecture. It helped me a lot to start my project on recommender system. :)
Excellent! Congratulation for your presentation!
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!
Great introduction!
Very nice and smooth introduction .. Thank you .. I hoped for a python code implementation as well
Incredible presentation! I semi-disagree with precision and recall being good evaluation metrics for a recommendation system using a masking technique to evaluate model performance during the offline training phase.
This is due to them demanding the output of the model to be binary, where as masked-prediction in this case would represent more of a regression problem leading RMSE to be a more valuable evaluation technique.
Great presentation though, very clear explanations.
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?
very crisp but makes the point.. thanks
How to deploy the model in a cloud platform and then consume in front end app like react. Thanks
what is a good value for sparsity
Excellent talk on Recommender System
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?
Nice talk !
Any software I can use instead of building my own?
How much u charge for making a video recommendation system for Android app?
Excellent presentation. Thanks
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?
I think we just need to optimize for new users only. stats.stackexchange.com/questions/320962/matrix-factorization-in-recommender-systems-adding-a-new-user
Very clear. Thanks
a good one, 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.
Hmm.. Jill Cates, sounds very much like Bill Gates.
There is nothing new to learn from this presentation. Don’t waste time!
Thank you, but for someone new this is very helpful, so maybe not a waste of time.
nothing new for whom? Maybe not for you, but maybe then you are not the target audience. Then just don't bother to watch the Video and take your business elsewhere
There are better videos out there who teach the same so don’t waste your time on this video. As simple as that. AutoML will kill data science jobs so I decided to move on to another technology. Personally , I would suggest learn blockchain because it’s plays a huge role in security and also ensuring any data exist inside a blockchain is tamper proof. ML + Blockchain = 💯💸🤑
@@joeljoseph26 you should not waste your time writing this stuff, which has nothing to do with this video and others shouldn't waste their time reading it.
Don't you waste anymore of other peoples time :)
what an arrogant rude person. Nothing new to you does not mean that no one else would find this useful. This is a very good helicopter view for someone who is new to build recommender system using collaborative filterings. If you REALLY value your time that much, you should probably not write this time-wasting comment. So yeah, you are just bitter and not happy with your own life, and leasing out on an innocent target.