The implementation of SiZeLet (SZL) proposed in the paper Everything Can Be Embedded by Zhili Shen.

1. Create ./output, ./cache, ./saved_models under the project folder
2. Download the Product Fit dataset from this link, and unzip it in the project folder.
3. Download the GloVE Word Embedding from this link, move it to the ./cache folder after unzipping glove.twitter.27B.zip.

1. You can configure SiZeLet in config.py, SiZeLet has these options:

• train_test_proportion: proportion of training set and validation set.
• TextCNNorBiRNN: use textCNN or BiRNN.
• max_length_sentence: the length of the longest sentence.
• min_frequency: words with word frequency lower than this number will be deleted.
• use_pretrained_model: whether to use a pre-trained model.
• user_embedding_dim: user discrete attribute embedding dimension.
• item_embedding_dim: item discrete attribute embedding dimension.
• review_embedding_dim: review discrete attribute embedding dimension.
• kernel_sizes: textCNN convolution kernel size.
• num_channels: textCNN channel size.
• num_hidden: BiRNN hidden layer dimension.
• num_layers: BiRNN hidden layer number.
• lr: learing rate.

2. Run train.py file

python train.py

3. The result will appear in ./output/output.txt