README.md

RTN

The code for the paper "Recurrent Translation-based Network for Top-N Sparse Sequential Recommendation", IEEE Access.

Requirements

We tested the code on:

• python 3.6
• pytorch 1.0.0

other requirements:

• numpy
• pandas

Usage

Run the code using the command:

python main.py -d [Dataset_Name]

The program will automatically detect CUDA, and train the model on a GPU if possible.

The trained model will be saved in "model/[Dataset_name]/RTN.pt".

Dataset

To use your own dataset, create csv file with filename "[Dataset_name].csv" in folder "data", where in each line is an interaction in format "[user_id],[item_id],[rating],[timestamp]".

For example, in "amz-video.csv":

A9RNMO9MUSMTJ,B000GIOPK2,2.0,1281052800
A2582KMXLK2P06,B000GIOPK2,5.0,1205884800
AJKWF4W7QD4NS,B000GIOPK2,3.0,1186185600
A153NZD2WZN5S3,B000GIPKWY,5.0,1273017600
...

Note that values of rating are unused in the program.

If a dataset contains repeatable interactions (a user interacted with an item multiple times), use option "--repeatable".

Citation

If our code is useful in your research, please cite:

N. Chairatanakul, T. Murata and X. Liu, "Recurrent Translation-Based Network for Top-N Sparse Sequential Recommendation," in IEEE Access, vol. 7, pp. 131567-131576, 2019, doi: 10.1109/ACCESS.2019.2941083.

Bibtex:

@article{8835015,
  author={Chairatanakul, Nuttapong and Murata, Tsuyoshi and Liu, Xin},
  journal={IEEE Access}, 
  title={Recurrent Translation-Based Network for Top-N Sparse Sequential Recommendation}, 
  year={2019},
  volume={7},
  number={},
  pages={131567-131576},
  doi={10.1109/ACCESS.2019.2941083}}