Comments (4)
也就是我不太懂gama^(k+1)(a1)、gama^(k+1)(a2)【全局图上的attention】和alpha^(k+1)(ab)、beta^(k+1)(ai)【局部图上的attention】计算方式的不同,为什么gama^(k+1)(a1)、gama^(k+1)(a2)将两个embedding全连接之后输入MLP计算attention,而alpha^(k+1)(ab)、beta^(k+1)(ai)分别使用只用户embedding、物品embedding输入MLP计算attention。
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二者的区别是node attention 与 graph attention的区别吗?因为用户是处于社交网络和兴趣网络上的,所以需要一个graph attention ,而物品只处于兴趣网络上,因此只需要node attention。
我想问,可以去掉user的node attention,只使用graph attention吗?也就是去掉中间变量p和q的计算过程。您考虑使用node attention,是因为实验效果更好吗?
from diffnet.
二者的区别是node attention 与 graph attention的区别吗?因为用户是处于社交网络和兴趣网络上的,所以需要一个graph attention ,而物品只处于兴趣网络上,因此只需要node attention。
我想问,可以去掉user的node attention,只使用graph attention吗?也就是去掉中间变量p和q的计算过程。您考虑使用node attention,是因为实验效果更好吗?
你好,我也注意到文章中的注意力是通过用户与用户交互的物品连接通过mlp得到注意力得分,但是代码中是直接将用户嵌入和用户的邻居嵌入输入mlp得到分数并且做归一化,这两个好像并不一样。我也想知道如果按照文章的说法用代码实现改如何去做,如何通过mlp计算用户和连接物品的注意力得分然后再做图卷积呢?
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@lijunweiyhn 已经@作者了,我不太清楚具体的实现哈。
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