akaxlh / khgt Goto Github PK

Hello I have a question about embedding of uEmbed0/iEmbed0 and UEmbedPred/IEmbedPred.

In "ours" function, why Embed0 is replaced by a random initialized EmbedPred in code line 125~126 in labcode_ml10m.py？I didn't find a description of it in the paper, and I don't understand what the motivation is for doing so.

I am confused.

Anyway, thanks of the codes. It is pretty.

你好，您这个源码里面少放了安装包的版本，使用tensorflow的一些版本来跑程序的时候，一直出错。

def GAT(self, srcEmbeds, tgtEmbeds, tgtNodes, maxNum, Qs, Ks, Vs):
	QWeight = tf.nn.softmax(NNs.defineRandomNameParam([args.memosize, 1, 1], reg=True), axis=1)
	KWeight = tf.nn.softmax(NNs.defineRandomNameParam([args.memosize, 1, 1], reg=True), axis=1)
	VWeight = tf.nn.softmax(NNs.defineRandomNameParam([args.memosize, 1, 1], reg=True), axis=1)
	Q = tf.reduce_sum(Qs * QWeight, axis=0)
	K = tf.reduce_sum(Ks * KWeight, axis=0)
	V = tf.reduce_sum(Vs * VWeight, axis=0)

I found above code to implement equation (3), I have 2 questions:

1. I think it should be below:

tf.nn.softmax(..., axis = 0) 

2. For all behavior $k$, there supposed to be only 1 group of $Q^h_m, m = 1\cdots, M$ to aggregated from, according to equation (3). While in practical implementation, each behavior $k$ has its own $M$ channels basis parameters.

Please excuse me if there is any misunderstanding.

作者您好！最近看了KHGT这篇论文觉得很有启发，但是在对比模型的实现上遇到了一些问题。请问能否发一份对比模型的代码，我的邮箱是[email protected]。如果可以的话，非常感谢！

Is it in the file "trn_catDict"

Hello, I have a question. In the "messagePropagate" function, why do srcNodes and tgtNodes look up their embeddings in the same embedding table?

Hi, Thank you for your quick response.
I was unable to run the code on retail dataset. (and Successfully run the code on ml10m and yelp datasets )
BTW, I have fixed the following minor issues to enable the correctness:

• change the folder name from 'Yelp' to 'yelp' (match the folder name in DataHandler_time.py)
• change line 14 in DataHandler_time.py to 'retail' (instead of 'Tmall')

During the execution of

python labcode_retail.py --data retail --graphSampleN 15000 --reg 1e-1 --save_path model_name

The error pops out due to the index out of range. I think these are some issues with the dataset.
Please let me know if you could get it run.
And would you mind to sharing me the original data and data preprocessing code?

Dear author, I would like to ask you some problems in datasets
that is, the user of each dataset exceed 10000, but I found that only 10000 users in test data
For example, there are 67788 users in ML10M, but only 10000 users are chosen for test, is it reasonable?
Thanks!