The source code for our DASFFA Submission 65 Paper "Learning Global and Multi-granularity Local Representation with MLP for Sequential Recommendation"

• Install Python, Pytorch(>=1.8). We use Python 3.7, Pytorch 1.8.
• If you plan to use GPU computation, install CUDA.

If you have downloaded the source codes, you can train the model just with data_name input.

python main.py --data_name=[data_name]

If you want to change the parameters, just set the additional command parameters as you need. For example:

python main.py --data_name=Beauty --num_hidden_layers=4 --batch_size=512

We present the optimal models on the three datasets and their experimental results. You can test the model has been saved by command line.

Beauty:

python main.py --data_name=Beauty --do_eval --load_model=MLM4Rec-Beauty-0 --hidden_size=192 --cmlp_type=3 --data_aug

Yelp:

python main.py --data_name=Yelp --do_eval --load_model=MLM4Rec-Yelp-0 --cmlp_type=2 --max_seq_length=30 --data_aug

ml-1m:

python main.py --data_name=ml-1m --do_eval --load_model=MLM4Rec-ml-1m-0 --cmlp_type=2 --max_seq_length=300 --hidden_dropout_prob=0.2

Additional hyper-parameters can be specified, and detailed information can be accessed by:

python main.py --help