Comments (2)
As introduced in the paper, LINE and node2vec would treat all nodes of different types as one node type, thus ignoring the heterogeneity of graph structure.
GCN and GAT would operate on the metapath-based homogeneous graphs, and report the result of the best metapath combination. E.g., one may use UAU metapath to obtain user embeddings, and ATA metapath to obtain artist embeddings.
HAN would be trained in a similar way as GCN and GAT, but utilizing multiple metapaths at the same time.
All of these methods use the dot product as a decoder to get plausibility scores of user-artist pairs.
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