KDD2020 Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion
pytorch==1.3.0, torch_geometric==1.3.2
To be honest, most of errors derive from the wrong installation of the two packages
The word embedding file word2vec_redial.npy can be produced by the following function dataset.prepare_word2vec(), or directly download from the netdisk https://drive.google.com/file/d/1BzwGgbUBilaEZXAu7e1SlvxSwcAfVe2w/view?usp=sharing
This model is trained by two steps, you should run the following code to pre-train the parameters by Mutual Information Maximization and then learn the recommendation task. Based on my experience, it will converge after 3 epochs pre-training and 3 epochs fine-tuning.
python run.py
Then you can run the following code to learn the conversation task. Limitted by the small dataset, Transformer model is difficult to coverge, so our model need many of epochs to covergence. Please be patient to train this model.
python run.py --is_finetune True
For convenience, our model will report the result on test data automatically after covergence.