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rnn_recsys's Issues

my auc simply not goes up

hi,

Thanks for your sharing.
i'm trying to apply your code to my toy recommendation project. During it, I've developed into 3 concerns:

  1. we have 300k Chinese titles dataset, and after several training epochs of autoencoder, the loss plateaus at 19k+, which is simply too high. Do you have any idea how to fine tune the autoencoder?
  2. I trained RNN with 50k samples, again, after several epochs, the auc goes back and forth around 0.53~0.54, Is there any way to fine tune the model?
  3. Is it a good idea to just fetch the trained user embedding, do the dot product with item embedding to recommend? thanks

TypeError: reduce_sum() got an unexpected keyword argument 'keepdims'

mldl@ub1604:/ub16_prj/rnn_recsys$ python3 train.py
launching the program...
1.4.0
Traceback (most recent call last):
File "train.py", line 233, in
train_RS()
File "train.py", line 124, in train_RS
my_model = RNNRS(**hparams)
File "/home/mldl/ub16_prj/rnn_recsys/models/RNNRS.py", line 35, in init
self.predictions, self.error, self.loss, self.train_step, self.summary = self._build_model()
File "/home/mldl/ub16_prj/rnn_recsys/models/RNNRS.py", line 65, in _build_model
preds = tf.sigmoid( tf.reduce_sum(tf.multiply(u_t, self.Item), 1, keepdims = True) + global_bias , name= 'prediction') ##--
TypeError: reduce_sum() got an unexpected keyword argument 'keepdims'
Exception ignored in: <bound method BaseRS.del of <models.RNNRS.RNNRS object at 0x7f77f5a6dba8>>
Traceback (most recent call last):
File "/home/mldl/ub16_prj/rnn_recsys/models/LinearAvgRS.py", line 29, in del
if self.log_writer:
AttributeError: 'RNNRS' object has no attribute 'log_writer'
mldl@ub1604:
/ub16_prj/rnn_recsys$

word_hashing_file

Hi ,I am a little confused about word_hashing_file = r'Y:\BingNews\Zhongxia\my\articles_wordhashing_3w.obj'.
Dose the word_hashing_file contain some context or is just empty? I don't have this file. Should I create one or the project will create this file automatically when it compiles?

Original dataset

hi
you have done a great work . can you please email or share the link of original complete dataset.

I will appreciate.

Thanks

about the train.txt

hi,dear
what's the meaning of the sentence ?
data/RS/train.txt: each line is a training instance, in the form of user_history \t target_item_id \t label. user_history is a sequence of item_id, splited by space.

user_history is only user clicks ?
target_item_id is the user last click ?
and if the last click is real click , the label is 1, else 0,

could u pls help me ?
thx

questions about the trainning data

hi,author
I have some questions about the training data.
in train.txt,What each line represents
and in article.txt,The first line and the second line are repeated eighft times.?

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