Abstract: Graph neural networks provide a powerful frame-work for recommendation systems by capturing dependencies through message passing and modeling user-item interactions using an adjacency matrix ...
Abstract: Contrastive learning (CL) has recently sparked a productive line of research in the field of recommendation, due to its ability to extract self-supervised signals from raw data that align ...
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