It is an extension of SASRec, which concatenates items and items’ features as the input.

Running with RecBole

Model Hyper-Parameters:

  • hidden_size (int) : The number of features in the hidden state. It is also the initial embedding size of items. Defaults to 64.

  • inner_size (int) : The inner hidden size in feed-forward layer. Defaults to 256.

  • n_layers (int) : The number of transformer layers in transformer encoder. Defaults to 2.

  • n_heads (int) : The number of attention heads for multi-head attention layer. Defaults to 2.

  • hidden_dropout_prob (float) : The probability of an element to be zeroed. Defaults to 0.5.

  • attn_dropout_prob (float) : The probability of an attention score to be zeroed. Defaults to 0.5.

  • hidden_act (str) : The activation function in feed-forward layer. Defaults to 'gelu'. Range in ['gelu', 'relu', 'swish', 'tanh', 'sigmoid'].

  • layer_norm_eps (float) : A value added to the denominator for numerical stability. Defaults to 1e-12.

  • initializer_range (float) : The standard deviation for normal initialization. Defaults to 0.02.

  • selected_features (list) : The list of selected item features. Defaults to ['class'] for ml-100k dataset.

  • pooling_mode (str) : intra-feature pooling mode. Defaults to 'sum'. Range in ['max', 'mean', 'sum'].

  • loss_type (str) : The type of loss function. If it set to 'CE', the training task is regarded as a multi-classification task and the target item is the ground truth. In this way, negative sampling is not needed. If it set to 'BPR', the training task will be optimized in the pair-wise way, which maximize the difference between positive item and negative item. In this way, negative sampling is necessary, such as setting training_neg_sample_num = 1. Defaults to 'CE'. Range in ['BPR', 'CE'].

A Running Example:

Write the following code to a python file, such as

from recbole.quick_start import run_recbole

run_recbole(model='SASRecF', dataset='ml-100k')

And then:



  • SASRecF is a sequential model that integrates item context information. selected_features controls the used item context information. The used context information must be in the dataset and be loaded by data module in RecBole. It means the value in selected_features must appear in load_col.

Tuning Hyper Parameters

If you want to use HyperTuning to tune hyper parameters of this model, you can copy the following settings and name it as hyper.test.

learning_rate choice [0.01,0.005,0.001,0.0005,0.0001]
attn_dropout_prob choice [0.2, 0.5]
hidden_dropout_prob choice [0.2, 0.5]
n_heads choice [1, 2]
n_layers choice [1,2,3]

Note that we just provide these hyper parameter ranges for reference only, and we can not guarantee that they are the optimal range of this model.

Then, with the source code of RecBole (you can download it from GitHub), you can run the to tuning:

python --model=[model_name] --dataset=[dataset_name] --config_files=[config_files_path] --params_file=hyper.test

For more details about Parameter Tuning, refer to Parameter Tuning.

If you want to change parameters, dataset or evaluation settings, take a look at