动态推荐场景下的图学习.pdf

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1、动态推荐场景下的图学习孙庆赟北京航空航天大学 计算机学院Homepage:https:/sunqysunqy.github.io/Email:sunqybuaa.eduOutline Background:Deep Graph Learning for Recommendation Dynamic Graph OOD:Environment-aware Dynamic Graph Learning Topology-Imbalance:Position-aware Graph Structure Learning Dataset Distillation:Structure-broadcast

2、ing Graph Condensation Privacy-Preserving Recommendation:Differential privacy for HGNN Conclusion2？Node classificationAd&ProductRecommendationFriend RecommendationnGraph Learning has been widely applied in online recommendationp E-commerce,Content Sharing,Social Networking,Forum User-User Connection

3、sUser-Item ConnectionsItem-Item ConnectionsPOI&PostRecommendationThe Era of Connected Worldlink predictionsubgraph classificationMethodologyBackgroundBackgroundChallanges for Graph LearningnDynamic&Open:Distribution shifts naturally exists in graph,and can be spatio-temporal.nImbalance:Graph-specifi

4、c topology imbalance leads to decision boundary shift.nLarge-scale:How to construct smaller-scale recommendation datasets for efficiently training?nPrivacy:Leakage of sensitive user informationGraph Data from multiple domainsDynamic Graph DataImbalanced topology distributionMethodologyBackgroundBack

5、groundOutline Background:Deep Graph Learning for Recommendation Dynamic Graph OOD:Environment-aware Dynamic Graph Learning Topology-Imbalance:Position-aware Graph Structure Learning Dataset Distillation:Structure-broadcasting Graph Condensation Privacy-Preserving Recommendation:Differential privacy

6、for HGNN Conclusion5nTasks on real-world graphs are challengingp Distribution shifts naturally exists in graph data,and can be spatio-temporalp Out-of-distribution(OOD)generalized GNNs are critically needed!ConclusionExperimentstraffic networkstransaction networksMethodologyBackgroundBackgroundDynam