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Knowledge Enhanced Multi-intent Transformer Network for Recommendation
However, simply integrating KG into recommendation usually brings in negative feedback in industry, due to the ignorance of the following two factors: i) users' multiple intents, which involve diverse nodes in KG.
Metapaths guided Neighbors aggregated Network for?Heterogeneous Graph Reasoning
Specially, MHN employs node base embedding to encapsulate node attributes, BFS and DFS neighbors aggregation within a metapath to capture local and global information, and metapaths aggregation to combine different semantics of the heterogeneous graph.
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