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Lightweight Compositional Embeddings for Incremental Streaming Recommendation
Most work in graph-based recommender systems considers a {\em static} setting where all information about test nodes (i. e., users and items) is available upfront at training time.
A Collective Learning Framework to Boost GNN Expressiveness
Graph Neural Networks (GNNs) have recently been used for node and graph classification tasks with great success, but GNNs model dependencies among the attributes of nearby neighboring nodes rather than dependencies among observed node labels.
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