Graph convolutional neural networks for web-scale recommender systems

1. Ho-Beom Kim
Network Science Lab
Dept. of Mathematics
The Catholic University of Korea
E-mail: hobeom2001@catholic.ac.kr
2023 / 11 / 20
YING, Rex, et al.
ACM SIGKDD 2018

2. 2
Introduction
Problem Statements
• NGCF is a model that deepens the use of the sub-graph structure with high-hop neighbors, and is a
model developed from GCN.
• NGCF specified user and item embedding through feature transformation, neighborhood aggregation,
and nonlinear activation based on the structure of GCN, but this structure was very heavy and
burdensome.
• In this paper, They proposed LightGCN, which overcomes these problems, and the results of comparing
NGCF and LightGCN are as follows. (Hop refers to a part of the path located between the source and
destination in the network structure, and in the graph, high-hop refers to how far away from the target
node.)

3. 3
Introduction
Contribution
1. They empirically show that two common designs in GCN, feature transformation and nonlinear
activation, have no positive effect on the effectiveness of collaborative filtering.
2. They propose LightGCN, which largely simplifies the model design by including only the most essential
components in GCN for recommendation.
3. They empirically compare LightGCN with NGCF by following the same setting and demonstrate
substantial improvements. In-depth analyses are provided towards the rationality of LightGCN from
both technical and empirical perspectives.

4. 4
Methodology
Overview of PinSage model architecture using depth-2 convolutions

5. 5
Methodology
Model Architecture

6. 6
Methodology
Overview of PinSage model architecture using depth-2 convolutions

7. 7
Methodology
Loss function

8. 8
Methodology
Random negative examples and hard negative examples.

9. 9
Methodology
Random negative examples and hard negative examples.

10. 10
Experiments
Hit-rate and MRR for PinSage and content-based deep learning baselines

11. 11
Experiments
Offline Evaluation

12. 12
Experiments
Probability density of pairwise cosine similarity for visual embeddings

13. 13
Experiments
Head-to-head comparison of which image

14. 14
Experiments
Head-to-head comparison of which image

15. 15
Experiments
Performance tradeoffs for importance pooling

16. 16
Conclusions
Conclusion
• They proposed PinSage, a random-walk graph convolutional(GCN)
• They introduced the use of importance pooling and curriculum training that drastically improved embedding
performance.
• They deployed PinSage at Pinterest and comprehensively evaluated the quality of the learned embeddings
on a number of recommendation tasks, with offline metrics, user studies and A/B tests all demonbstrating a
substantial improvement in recommendation performance.
• Their work demonstrates the impact that graph convolutional methods can have in a production
recommender system, and they believe that PinSage can further extended in the future to tackle other
graph representation learning problems at large scale, including knowledge graph reasoning and graph
clustering