Comments (1)
I agree with you. Simply tuning the parameters based on the performance of testing data could lead to overfitting problem.
However, if you read the original keras implementation, you would find no validation phrase, either. Therefore I removed the parameter searching part since the sampled interaction raises much uncertainty.
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Related Issues (20)
- How to extract prediction from this model? HOT 2
- Data files? HOT 1
- RuntimeError: An attempt has been made to start a new process before the current process has finished its bootstrapping phase.
- Concatenation of user and item embeddings HOT 2
- what is the meaning of num_neg and test_num_neg?
- why did you use kaiming_uniform to init parameters? HOT 1
- a error about use pretraining HOT 3
- Do your code with pytorch have the HOT 1
- Why I use the same code and parameters,the result is not as good as yours? HOT 5
- another issue HOT 1
- It seems that you didn't use the dataset of test_rating,can you show why? HOT 1
- 向量生成 HOT 2
- user_vector HOT 1
- Broken pipe error
- Haven't used sigmoid in the output of last layer. HOT 1
- main_path和model_path
- The negative sampling method has a great influence on performance HOT 3
- AssertionError: lack of GMF model HOT 3
- GMF better than BPR HOT 3
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