Thank you for your sharing.

However, we found a very serious problem in the DHCN, the labels were leaked during the testing.

As mentioned in the paper, a sequence splitting method is used to augment and label the dataset. Figure 1 shows an example of the data after preprocessing.

Notice that, the data is not shuffled during the training and testing in DHCN, which means the last item in the previous sequence is the target item of the current sequence. (The target of each sequence is shown in Figure 2.)

In DHCN, you convert each batch (100 sequences) into a hypergraph, and the prediction for each sequence in the current batch is based on the items in the sequence and the hypergraph. However, previous sequences contain both the items and the target item of the current sequence, which means in the hypergraph the items of each sequence directly connect with the target item (The co-occur of items in previous sequences makes the target item have a great influence on the current prediction according to the Equation 2 in the paper).

In my opinion, we should not make predictions with the help of neighbor sequences in the test data, unless we shuffle the dataset or do not use the sequence splitting method to augment and label the dataset. (CSRM uses neighbor sequences to help predictions, but it shuffles the dataset before training and testing.)

This is the result of DHCN we obtained on Diginetica after data shuffling: Prec@20: 49.14, MRR@20: 16.05

Hi, thanks for sharing this interesting work.

I am confused about the generation of session embedding during the training and testing phases. Specifically, during the training, we can generate a session embedding via the hypergraph, which means we use the interactions in the other sessions to learn the embedding for items within the current batch.

When it comes to the testing, we only get access to the interactions within the current session, without the interactions from the other sessions. However when I read the code, I found no matter in the training or testing phase, the function build will always go through the hypergraph conv layer, and then it generates an embedding for the current session, as refers to, https://github.com/xiaxin1998/DHCN/blob/aeb54db1057b668dab871cd9ea712dc4e90daec6/model.py#L129

I am not sure if I miss some key details, but I find it is hard to understand the current implementation in generating session embedding during the testing phase. Would you mind clarifying it?

Excuse me, at present, I plan to add some other information in the data set to the session-based recommendation task to improve the recommendation accuracy (such as timestamp), and plan to cite and compare your DHCN model. According to what you mentioned in the paper, I have downloaded the original data of the data set Tmall from IJCAI-15 competition. The original Tmall data set contains the information I want to use. May I ask you about the method of preprocessing the Tmall raw data? If you can send me the preprocessing code, I would be very grateful, thank you.

Excuse me, why the code is gone?Excuse me, why is the result of running the author's code very different from that in the paper (for example, diginetica dataset, P@20 value is about 50 in the paper , but recall@20 value is about 17 in the code).Is it because it is not set correctly?

https://github.com/xiaxin1998/DHCN/blob/b798a7ba69c95bc68209feb493b23a07ed8b57ad/util.py#L40

A warning will appear on this line of code:

RuntimeWarning: divide by zero encountered in true_divide
  DH = H.T.multiply(1.0/H.sum(axis=1).reshape(1, -1))

There is an element with a value of 0 in H.sum(axis=1), this will eventually lead to the generation of inf. Doesn’t it affect the model data?

I only tested the model in the diginetica dataset, using the original code and the preprocessed data provided by Dropbox. I guess the preprocessed code should be the same as SR-GNN, but the results I ran are abnormal. I hope you can provide some suggestions.

The datasets downloaded from link "https://www.dropbox.com/sh/j12um64gsig5wqk/AAD4Vov6hUGwbLoVxh3wASg_a?dl=0" are garbled.I tired to solve this problem by changing the encoding format and other ways,but it still goes wrong.Could you please send me a dataset?My email address is [email protected] will be appreciated for your reply, thank you very much!

i want to test my model in the sample dataset(Mentioned in your code)，I didn’t find it in the dataset link, where can I get it, or the sample is generated according to certain rules. It would be appreciated if you could provide it!!

(pytorch) XXX@xxxxx:/DATA/XXX/DHCN$ python main.py --dataset Tmall --beta 0.02
Traceback (most recent call last):
  File "main.py", line 5, in <module>
    from util import Data, split_validation
ImportError: cannot import name 'Data' from 'util' (/DATA/XXX/DHCN/util.py)
(pytorch) XXX@cvpruser:/DATA/XXX/DHCN$ python main.py --dataset Tmall --beta 0.02
Namespace(batchSize=100, beta=0.02, dataset='Tmall', embSize=100, epoch=30, filter=False, l2=1e-05, layer=3, lr=0.001)
/home/XXX/anaconda3/envs/pytorch/lib/python3.8/site-packages/numpy/core/_asarray.py:102: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
  return array(a, dtype, copy=False, order=order)
-------------------------------------------------------
epoch:  0
start training:  2021-09-17 10:49:17.124433
Traceback (most recent call last):
  File "main.py", line 71, in <module>
    main()
  File "main.py", line 53, in main
    metrics, total_loss = train_test(model, train_data, test_data)
  File "/DATA/XXX/DHCN/model.py", line 180, in train_test
    targets, scores, con_loss = forward(model, i, train_data)
  File "/DATA/XXX/DHCN/model.py", line 168, in forward
    item_emb_hg, sess_emb_hgnn, con_loss = model(session_item, session_len, D_hat, A_hat, reversed_sess_item, mask)
  File "/home/XXX/anaconda3/envs/pytorch/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
  File "/DATA/XXX/DHCN/model.py", line 153, in forward
    session_emb_lg = self.LineGraph(self.embedding.weight, D, A, session_item, session_len)
  File "/home/XXX/anaconda3/envs/pytorch/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
  File "/DATA/XXX/DHCN/model.py", line 75, in forward
    session_emb_lgcn = np.sum(session, 0)
  File "<__array_function__ internals>", line 5, in sum
  File "/home/XXX/anaconda3/envs/pytorch/lib/python3.8/site-packages/numpy/core/fromnumeric.py", line 2247, in sum
    return _wrapreduction(a, np.add, 'sum', axis, dtype, out, keepdims=keepdims,
  File "/home/XXX/anaconda3/envs/pytorch/lib/python3.8/site-packages/numpy/core/fromnumeric.py", line 87, in _wrapreduction
    return ufunc.reduce(obj, axis, dtype, out, **passkwargs)
  File "/home/XXX/anaconda3/envs/pytorch/lib/python3.8/site-packages/torch/_tensor.py", line 643, in __array__
    return self.numpy()
TypeError: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.

model.py的第40行“ item_embeddings = np.sum(final, 0) / (self.layers+1)” 报错： TypeError: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.

Process finished with exit code 1

In file "model.py" ,line 51, in forward item_embeddings = np.sum(final, 0)

RuntimeError: Can't call numpy() on Tensor that requires grad. Use tensor.detach().numpy() instead.