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STACP

Joint Geographical and Temporal Modeling based on Matrix Factorization for Point-of-Interest Recommendation (ECIR 2020)

Environment Settings

  • Python version: '2.7'
  • You have to install the required libraries

To run the code

You need just run the recommendation.py

The TimeAwareMF.py lib is implemented in Python 2. Therefore you should run the model with Python 2.

  • To change the dataset, you have to write its name in the recommendation.py.

Cite

Please cite our paper if you use our datasets or implementations:

@inproceedings{rahmani2020joint,
  title={Joint geographical and temporal modeling based on matrix factorization for point-of-interest recommendation},
  author={Rahmani, Hossein A and Aliannejadi, Mohammad and Baratchi, Mitra and Crestani, Fabio},
  booktitle={European Conference on Information Retrieval},
  pages={205--219},
  year={2020},
  organization={Springer}
}

This repository contains the implementation of the Joint geographical and temporal modeling based on matrix factorization for point-of-interest recommendation presented in the ECIR 2020 paper. More details will be updated later.

Acknowledge

For implemenation we got some information and inspiration of the codes that provided by the following paper:

Liu, Yiding, et al. "An experimental evaluation of point-of-interest recommendation in location-based social networks." in VLDB, 2017

Contact

If you have any questions, do not hesitate to contact us by srahmani@znu.ac.ir or rahmanidashti@gmail.com, we will be happy to assist.

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Joint Geographical and Temporal Modeling based on Matrix Factorization for Point-of-Interest Recommendation - ECIR 2020

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