TIFUKNN

This is our implementation for the paper:

Haoji Hu, Xiangnan He, Jinyang Gao, Zhi-Li Zhang (2020). Modeling Personalized Item Frequency Information for Next-basket Recommendation.Paper in ACM DL or Paper in arXiv. In the 43th International ACM SIGIR Conference on Research and Development in Information Retrieval.

Please cite our paper if you use our codes and datasets. Thanks!

@inproceedings{hu2020modeling,
  title={Modeling personalized item frequency information for next-basket recommendation},
  author={Hu, Haoji and He, Xiangnan and Gao, Jinyang and Zhang, Zhi-Li},
  booktitle={Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval},
  pages={1071--1080},
  year={2020}
}

Author: Haoji Hu

Environment Settings

• Python version: '3.6.8'

A quick start to run the codes with Ta-Feng data set.

python TIFUKNN.py ./data/TaFang_history_NB.csv ./data/TaFang_future_NB.csv 300 0.9 0.7 0.7 7 10

TaFang_history_NB.csv contains the historical records of all the customers. TaFang_future_NB.csv contains the future records of all the customers. The 300 is the number neighbors. 0.9 is the time-decayed ratio within each group. The first 0.7 is the time-decayed ratio accross groups. The second 0.7 is the alpha for combining two parts in prediction. 7 is the group size. 10 is the top k items recommened.