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Awesome talk ! One question: Would factorising the PMI matrix take care of the popularity bias ?

Awesome talk

By far this is the worst explanation of knowledge graphs i have seen on the internet. Absolutely no details on how to build a KG using the dataset.

The Chilean restaurant is the best restaurant of Chile.

Nice one. Thanks for sharing this. Very helpful!

The other person seems to be speaking in lowercase letter :) .Still pretty cool explanation

Thanks Kim! Loved your book too.🎉

Tx so much🎉

Superb one🎉

Brilliant one. Tx so much❤

Do you have any open source code? Where can I find it?

great tutorial!

Nice talk!

Not sure if I agree with the arguments on slide 4 about sequence continuation vs item masking. You can use the data efficiently by using back-propagation through time for sequence continuation training method.

This is great, thank you for sharing! Is there a github repo?

An amazing presentation, very well explained, thank you!

Excellent talk!

😳 Promo sm

😶‍🌫

Will the full talk be available at some point?

How could I get the material?

could you share the link for this notebook?

This is pure gold!

Can anyone explain from where is he getting softmax vectors for each video ? Softmax is only one output and not k outputs. Why are we doing nearest neighbor search by doing dot product of the user vector with the "so-called" softmax vectors. Why can't we directly take top K from softmax output /

They first 6 minutes had audio issues

20:14 system architecture 24:13 feature generation 25:47 model training 28:32 online ranking/users

Hi, How can I access public dataset for Dressipi, Thanks!

how do they take care of data imbalance , given clicks are just 0.5% of total impression count !

amazing work- Thx

The audio on this video is extremely quiet. Is there a version anywhere with better audio?

Thanks in a million.Great content.

Thanks in a million.Great content.

「画像が不快すぎる」、

great tutorial!

important and interesting research topic

This workshop is about conversational recommendation, not bias issues and solutions.

sorry，i still dont understand，can you explain Pessimistic Reward Models for Off-Policy Learning in Recommendation with some simple examples?

The person who asked the question at the end of the presentation didn't clarify or even use simple words the first or even second time!

Thanks team if possible can you please share us the code and PPT

26:36 wtf voice?!

Interesting

Thanks for the upload

i don't think this video is about bias issues?

🍀 𝚙𝚛𝚘𝚖𝚘𝚜𝚖

Hi! Seems that the content of the video does not match the title 🤔

Hello, could you turn the subtitle option on? It's a little hard to follow the English, please.

Dear, could you send me this presentation in PPT or PDF forms. Thank you in advance.

15:24 - Recommendation Engine @H&M 2:28:00 - Size fit recommendation 3:38:00 - Fashion Recommender Systems - Julian McAuley

thank you youtube recommender system

Thanks