Recommender Systems using Graph Neural Networks

[@daniamaya]

Your clarity solved a bunch of my open questions that arose digging into the topic. Thank you!

[@SickegalAlien]

Collaborative Filtering is such an underrated computation method, imho Many real life problems can be re-interpteted as a recommendation problem. It's just a matter of perspective, but the results can be huge!

[@mrprime557]

Awesome !!!! I was breaking my head to structure the vast information available on GNN. Love this video and ily bro !

[@DeepFindr]

Haha ily2

[@smitshah6665]

your content and explanation is Incredibly helpful. Thank you

[@samirelzein1095]

that s an amazing job! clearly honest deep effort i ncompliling such huge info in a compact and unified language! Congrats on this level of sophistication! Predict an even brighter future for you!

[@DeepFindr]

Thank you very much for the kind words!

[@aiml84]

As usual Awesome! Just waiting for credit card fraud detection video so that I can learn to transform a tabular data to graph data

[@clayouyang2157]

long time no see, i am glad to watch video released by you

[@DeepFindr]

Thank you :) how are you? Hope everything is fine

[@foxlikescat5508]

great explanation and overview as always :D

[@withmeeeeT]

I like your videos so much, so plz keep going on. My question is how can I build a simple model of a recommendation system using graph convolutional network?

[@mydigitalwayia956]

Felicitaciones, un trabajo excepcional. Saludos

[@goelnikhils]

Amazing Content. Exceptional

[@harisjaved1379]

Wonderful explanation!

[@TheAnna1101]

Thanks great video!

[@yerznkanyan]

Great video. Thanks !!

[@mohamedemmimoun3665]

I loved it thank you very much

[@skanderbegvictor6487]

this is gold

[@title601a]

It's very helpful , Thank a lot

[@shattered2024]

how did you find these papers? I'm interested in developing a recommendation system for creating outfits for a person i.e. you add a shirt to your shopping cart, the recommender will show pants, shoes, and belts that match the shirt

[@DeepFindr]

Which papers are you referring to? :) Just did a search on Google scholar

[@meylyssa3666]

Can you please do a tutorial on edge classification, please? Practical implementation I mean.

[@DeepFindr]

I have a video on node classification :) it's pretty much exactly that, only that the head of the network is different. Instead of one output per node, you have one output per possible edge :)

[@keyuchen3738]

Great job

[@jonimatix]

Do you have any links to blog post / examples showcasing these methods?

[@DeepFindr]

I've seen this post on medium: medium.com/decathlontechnology/building-a-recommender-system-using-graph-neural-networks-2ee5fc4e706d

[@jonimatix]

@@DeepFindr thanks so much, keep up the good work

[@auroraevans2918]

may i ask could we build a decision support system based on knowledge graphs using graph neural networks?

[@DeepFindr]

Sure, I think nothing speaks against trying it :) Many people use GNNs on knowledge graphs so it might also work for DSS's

[@wilfredomartel7781]

Maybe a practical example?

[@fatigseyno4430]

I must say your videos are great please keep up the good work. My question is how can i implement a simple recommender system with pytorch geometric. I have a custom dataset with two nodes: movies and user. Furthermore there is an edge that describes only the relation of both nodes. I want to create a bipartite graph like the one from kipf & welling and do a similarity analysis. I know how to get the data into the right format. But since I am new to the subject, I am having a hard time implementing a simple gnn model. If anyone here could help me I would be very grateful.

[@DeepFindr]

Hi! There is a section on bipartite graphs in Pytorch geometric: pytorch-geometric.readthedocs.io/en/latest/notes/batching.html?highlight=Bipartite#bipartite-graphs Also I have a couple of videos on how to create custom datasets. Let me know if you need further help!

[@fatigseyno4430]

@@DeepFindr Hello, thank you for the quick feedback. I have now watched many of your videos again. I know how to create a bipartite garph with Heterograph(). What I didn't really understand is which layer to use for message passing and how the structure of the GNN model should look like. I have only one edge type and would like to have a simple recommneder system for similiarity analysis.

[@DeepFindr]

Thats something you need to figure out experimentally. You can start with GCNConv and then try others. :) Simply stack a couple of layers and build a network head for link prediction. That means - use the final node level embeddings, concatenate them according to the edges and predict one value per edge. Good luck!

[@stanislavshubin3447]

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