The code for the paper "Recurrent Translation-based Network for Top-N Sparse Sequential Recommendation", IEEE Access.
We tested the code on:
- python 3.6
- pytorch 1.0.0
other requirements:
- numpy
- pandas
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".
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".
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}}