3 accepted papers
Dynamic Graph Neural Networks (DyGNNs) are facing challenges with distribution shifts between training and test data that are similar but not identical. Existing DyGNNs for out-of-distribution scenarios primarily focus on discovering invariant patterns in the spatial domain, overlooking its impact o
The recommendation based on dynamic graph sequences aims to reveal complex evolutionary patterns in user-item interactions. Existing methods make predictions by encoding attribute contents through similarity but lack dynamic modeling of fine-grained attribute scenarios, resulting in a deviation in u…
Heterogeneous Graph Neural Networks(HGNNs) are widely regarded as an effective tool for modeling data with graph structures in recommendation. Current research lacks modeling of user attribute and project attribute distribution preferences, limiting graph structure optimization potential. In respons…