PinSage - Graph Convolutional Neural Networks for Web-Scale Recommender Systems | Paper Explained

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In this video, I do a deep dive into the PinSage paper!

It was the first application of the GNN as a huge scale recommender system such as the one at Pinterest.

You'll learn about:
✔️All the nitty-gritty details behind PinSage

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✅ paper: https://arxiv.org/pdf/1806.01973.pdf
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⌚️ Timetable:

00:00 Intro to PinSage and Pinterest
02:40 High-level overview of engineering innovation
05:40 MapReduce framework - a quick overview
08:00 Quick overview of the algorithm
09:10 High-level overview of algorithmic innovation
10:15 Tasks and related work
13:00 Problem setting (bipartite graph)
14:40 Detailed explanation of the algorithm
18:00 Neighborhood sampling and importance pooling
21:45 Model training and supervised data
23:50 Max margin ranking loss function explanation
29:18 Negative (hard) samples and curriculum learning
35:30 Pin features and baselines description
39:00 Offline evaluations and metrics (hit-rate, MRR)
43:00 Analysis of the embedding space (cosine sim)
45:15 User studies and A/B testing
49:00 Embedding space visualizations (t-SNE)

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#pinsage #graphneuralnets #recommendersystems

Видео PinSage - Graph Convolutional Neural Networks for Web-Scale Recommender Systems | Paper Explained канала Aleksa Gordić - The AI Epiphany