\SDM-master\code\train\run.py

def run_validating(hparams, model):
acc_keys = ["user_id", "ds", "user_embedding_output"]

#  user defined function
#  you should write your own code here for reading and writing data
**train_file = get_your_train_files()
test_file = get_your_test_files()
writer = open_your_test_result_file()**

不太明白需要什么样的数据格式，是否需要转换，恳请指教。

Hi,fuyu:
Great job for this match framework. However,I'm a little confused about the label generation logic of JD offline datasets as provided in your codes.
If convenient, would you please upload the preprocessing script of the offline dataset. Thx a lot.

请问你们京东数据集中各个子段的含义是什么，比如说action.csv中的type的每个取值是什么意思

hi, thanks you for open source code. there is two problems

1. is the same prefer feature filtered, such as item_id, brand_id, but not for short feature?
2. Is it convenient to provide the code to generate tfrecord from the original csv file?

I'm a new one who comes first into the recommender system. I happen to find this work and believe it is excellent and valuable. I want to learn from you and could you impart me some experience?

Please upload the datasets as well.

您好，最近在研究您的sdm源码，有一点疑惑：样例数据中num_labels=5，是把每个session最后的5个item作为目标吗？

通过sampled_softmax_loss负采样，inputs=rnn_outputs_split,是用户长短兴趣结合后各个时刻embedding组成的矩阵，那么这边的loss是session中各个时刻都会针对目标num_labels=5进行负采样，然后将所有loss相加？