The implementation of AR-Trip(Anti Repetition for Trip Recommendation). We devise a cycle-aware framework to mitigate the repetition, including drifting, guiding and adapting.
We run the code on the device with RTX3060(12 GB), i5 12400F, and 16G memory. Please Install the dependencies via anaconda:
conda create -n AR-Trip python=3.9.18
conda activate AR-Trip
conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.6 -c pytorch -c nvidia
conda install numpy pandas
pip install scikit-learn
| Folder Name | Description |
|---|---|
| asset | Metadata and preprocessing process |
| results | Storage related experimental results |
| src | the source code of AR-Trip |
| README.md | This instruction document |
| run.bat | The necessary command-line parameters for running |
The detailed operation mode and parameter settings of each model can be found in run.bat.
@echo off
REM Setting Python Interpreter Path
set python_path=(alter to your python path)
python .\src\run.py --dataset Osak --lr 0.001 --batch_size 4 --d_model 32 --decoding_type Adapting --training_type Penalty --Drifting --Guiding
python .\src\run.py --dataset Glas --lr 0.001 --batch_size 4 --d_model 32 --decoding_type Adapting --training_type Penalty --Drifting --Guiding
python .\src\run.py --dataset Edin --lr 0.001 --batch_size 16 --d_model 32 --decoding_type Adapting --training_type Penalty --Drifting --Guiding
python .\src\run.py --dataset Toro --lr 0.001 --batch_size 8 --d_model 32 --decoding_type Adapting --training_type Penalty --Drifting --Guiding
If your operating system is Windows, you can use the command In the working directory as
.\run.bat
to run this script file directly. You can also directly paste commands into the terminal to run the program just like
python .\src\run.py --dataset Glas --lr 0.001 --batch_size 4 --d_model 32 --decoding_type Adapting --training_type Penalty --Drifting --Guiding
Hope such instruction could help you with our projects. Any comments and feedback are appreciated.