MCMG

This repository is the implementation of MCMG.

MCMG is short for 'A Multi-Channel Next POI Recommendation Framework with Multi-Granularity Check-in Signals'. It is a novel framework, equipped with three modules (i.e., global user behavior encoder, local multi-channel encoder, and region-aware weighting strategy), which is designed to achieve the next POI prediction by fusing multi-granularity check-in signals from two orthogonal perspectives, i.e., fine-coarse grained check-ins at either POI/region level or local/global level.

File Descriptions

• dataset/
  • CAL/
    • AdjacentMatrix_CAL.txt: the adjacent matrix of Calgary;
    • CAL_checkin.csv: check-ins information of Calgary;
    • test_CAL.txt: test set of Calgary;
    • test_group_label_CAL.txt: group lables of trajectories in test set;
    • train_CAL.txt: training set of Calgary;
    • train_group_label_CAL.txt: group lables of trajectories in training set;
  • NY/
    • NY.rar: all the data files of New York;
  • PHO/
    • PHO.rar: all the data files of Phoenix;
  • SIN/
    • SIN.rar: all the data files of Singapore;
• appendix.pdf: the best parameter settings for all methods;
• main.py: main file;
• model.py: MCMG model file;
• parameter_setting.py: parameter settings file;
• utils.py: utils file.

More Experimental Settings

• Environment
  • Our proposed MCMG is implemented using pytorch 1.7.1, with Python 3.6.3 from Anaconda 4.3.30. All the experiments are carried out on a machine with Windows 10, Intel CORE i7-8565U CPU, NIVIDA GeForce RTX 2080 and 16G RAM. The following packages are needed (along with their dependencies):
    • cuda==11.0
    • hyperopt==0.2.4
    • numpy==1.19.5
    • pandas==1.1.5
    • progressbar==2.5
    • python==3.6.3
    • scipy==1.5.4
    • torch==1.7.1
• Data Preprocessing
  • Following state-of-the-arts, for each user, we chronologically divide his check-in records into different trajectories by day, and then take the earlier 80% of his trajectories as training set; the latest 10% of trajectories as the test set; and the rest 10% as the validation set. Besides, we filter out users with less than three trajectories.

How To Run

$ python main.py (note: use -h to check optional arguments)