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MixGCF for large graph version? #7
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Sorry for the late reply. The time cost of training on a graph comes from the aggregation process of GNN. A way to accelerate the sampling process of MixGCF is re-using the inner product between user-item and updating the scores during the training. In this way, many computations can be saved but it may introduce bias. |
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Hi! thank you for the great work. I was wondering if there is a kind of solution to make this work adaptive to large user-item graph?
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