Introducethe idea of synthesizing negative samplesrather than directly sampling negatives from the data for improving GNN-based recommender systems. Present a general MixGCF framework with the hop mixing and positive mixing strategies that can be naturally plugged into GNN-based recommendation models. Dem...
MixGCF: An Improved Training Method for Graph Neural Network-based Recommender Systems https://keg.cs.tsinghua.edu.cn/jietang/publications/KDD21-Huang-et-al-MixGCF.pdf 1. 背景 GNN在协同过滤相关方法中达到了最优的效果,从隐式反馈中负采样是协同过滤中需要面临的一大难题。当前在基于图的协同过滤方法...
Using the external drainage stent tube, which is routinely placed in every case in our facility, bile was collected at POD1-7, 14, 21, 28, or everyday in some cases. Exosomes were isolated by the fi lter device, and poly(A)+ mRNAs were purifi ed by oligo(dT)-immobilized...
Mix-GCF设计了两种策略:正混合和跳混合。在正混合中,作者引入了一种插值混合方法,通过注入来自正样本的信息来污染原始负样本的嵌入。在跳混合中,作者对一些原始负样本进行采样,例如图1中的 , 以及 ,然后通过使用他们选中的n跳邻居中聚合成的污染型嵌入来生成合成的负嵌入。详细的两种策略将在下面模块介绍。 图1 ...
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However, existing GNN-CF models only focus on one of them and ignore the other. Aiming to solve the two problems in a unified framework, we propose a Multi-Mixing strategy for GNN-based CF (M2GCF). In the main task, M2GCF perturbs embeddings of users, items and negative items with ...