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Adversarial Point-of-Interest Recommendation (APOIR)

This is the python implementation -- a POI recommendation model using adversarial training.


Requirements

  • python2.7
  • tensorflow >= 1.3.0
  • numpy >= 1.14.0

Datasets

Datasets         Users   POIs Check-ins
Gowalla    18,737 32,510 1,278,274
Foursquare    24,941 28,593 1,196,248
Yelp 30,887 18,995 860,888

For the Gowalla dataset, we filter out those users with fewer than 15 check-in POIs and those POIs with fewer than 10 visitors. For Foursquare and Yelp, we discard those users with fewer than 10 check-in POIs and those POIs with fewer than 10 visitors. We partition each dataset into training set and test set. For each user, we use the earlier 75% check-ins as the training data and the most recent 25% check-ins as the test data. All datasets are very sparse (the frequency of most POIs being visited is extremely low).


Baseline


Usage

  • To run APOIR, please first clone the code to your python IDE (eg:Pycharm), then run the command: python APOIR.py.

  • To customize the code,

    • you can change the Embedding_size in line 9
    • You need to set your data set folder in line 14
    • Then change the user number and POI number to fit the given data set in line 10 and line 11
    • You can also set the learning_rate_value to be a different value in line 17
  • You can use your own data sets in the source code

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