9. Future Research Directions and Open Issue

1. Diverse and Uncertain Representation

Representation이 다양한 경우와 불확실한 경우가 있음
Multiple vector approach
이러한 경우에 user를 onefold vector로 표현하기는 어려움
Multiple vector로 확장
Disentangled representation learning, capsule networks
DGCF: Orthogonal한 vector가 만들어지도록 유도
Density representation
Uncertainty를 더 잘 encoding
Gaussian embedding이라고 볼 수 있음

2. Scalability of GNN in Recommendation

기존의 GNN을 그대로 적용하기는 어려움
large memory usage, long training time
Reduce size of the graph
Sampling method
GraphSAGE: random sampling
PinSage: random walk
Small subgraph를 만들기도 함
Decouple the operations of nonlinearities and collapsing weight matrices
Neighbor-averaged features need to be precomputed only once
limited choice of aggregators and updaters

3. Dynamic Graphs in Recommendation

Relationship changes over time
GraphSAIL: Only example, incremental learning

4. Reception Field of GNN in Recommendation

Graph diffusion-based works: aggregation과 update를 분리
더 큰 reception field에 대비
너무 큰 reception field는 oversmoothing problem 야기
추천에서 node degree distribution은 long tail
Adaptive decision-based propagation step

5. Self-supervised Learning

데이터의 sparsity 관점에서 유리함
Contrastive learning task
Maximized agreement
Maximize mutual information

6. Robustness in GNN-based Recommendation

GNN이 noise에 취약함
Graph adversarial learning
Jointly learn the rating prediction and fraudster detection

7. Privacy Preserving

Federated learning 기반으로 해야 함
Federated learning과 high-order connectivity 정보를 같이 받는 것이 어려움
Pseudo interacted item을 추가하여 privacy 증진 가능
성능이 하락
PPGRec: Differential privacy로 문제 일부해결

8. Fairness in GNN-based Recommender System

GNN이 특정 item만 너무 추천하는 경우
User demographic에 따라 추천 성능이 너무 달라지는 경우
NISER, FairGNN 등의 연구 존재

9. Explainability

Instance-level method
Example-specific explanations
Identify important feature
Model-level methods
Generic understanding of how deep graph model works