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Found 3 papers, 1 papers with code
M2GNN: Metapath and Multi-interest Aggregated Graph Neural Network for Tag-based Cross-domain Recommendation
(2) From the CDR perspective, not all inter-domain interests are helpful to infer intra-domain interests.
AMinerGNN: Heterogeneous Graph Neural Network for Paper Click-through Rate Prediction with Fusion Query
Paper recommendation with user-generated keyword is to suggest papers that simultaneously meet user's interests and are relevant to the input keyword.
Click-Through Rate Prediction
Graph Representation Learning
+1
Knowledge graph enhanced recommender system
This is attributed to the rich attribute information contained in KG to improve item and user representations as side information.
