STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems, IJCAI 19
[Paper Review] STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems, IJCAI 19, Zhang et al
Goal
Inductive(and Transductive) User-Item rating(Matrix Completion) via GNN
Challenge
- Generalize model to predict unseen nodes embeddings for cold start problem
- one-hot vector(embedding look-up table)를 쓰면 unseen node에 대한 generalize가 잘 안 되고, cold-start problem을 야기하는 문제 발생
- Label leakage issue
- ex) user1과 item1 사이에 edge가 있을지 여부를 predict하는 task를 풀고 있는데, 애초에 aggregation 과정에서 neighbor의 표현을 토대로 update를 하기 때문에, label leakage가 발생하여 overfitting으로 이어짐
Solution
- Mask & Reconstruction strategy
- Sample & Remove strategy
Method
- STAR-GCN(STAcked and Reconstructed Graph ConvolutionalNetworks)
- Masked & Reconstruct
- To generalize embedding for new nodes,
- masking some percentage of input nodes
- reconstructing the clean node embeddings
- To generalize embedding for new nodes,
- Sample & Remove
- rating pairs를 fixed size만큼 샘플링하고, 해당 edge를 graph에서 remove함
- Multi-Block graph Encoder-Decoder architecture
- Block = Encoder + Decoder
- Block Stacking : 각 블록 별로 별개의 parameter 사용
- Recurrence : parameter sharing for across all blocks
- Encoder
- Generate node representations
- Multi-link GCN aggregator (which is used in GC-MC)
- Decoder
- Recover input node embeddings
- 2-layer fc layers
- Block = Encoder + Decoder
- Optimization
- Jointly training for the rating prediction(main task) and reconstruction(auxiliary) loss
ETC.
- Inductive와 Transductive 둘 다 가능
- 지난 번 논문도 그렇고, GNN쪽에서 inductive를 가능케 하기 위한 접근으로 embedding reconstruction을 취하는 경우가 많은 것 같네
- Task
- Matrix Completion via GNN
- Main task : link prediction
- Auxiliary task : Embedding reconstruction
- Matrix Completion via GNN
- GC-MC 훑어보기
- 깃허브로 블로그 이사하기!
Source
- https://www.ijcai.org/proceedings/2019/0592.pdf
- STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems, IJCAI 19, Zhang et al
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