Awesome
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
- USG
- MGMPFM
- LFBCA
- iGSLR
- LORE
- IRenMF
- GeoMF
- RankGeoFM You can find all the above code at http://spatialkeyword.sce.ntu.edu.sg/eval-vldb17/
- GeoTeaser You can find this code at https://github.com/shenglin1987/geo_teaser
- PACE You can find this code at https://github.com/yangji9181/PACE2017
Usage
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To run APOIR, please first clone the code to your python IDE (eg:Pycharm), then run the command: python APOIR.py.
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To customize the code,
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- you can change the Embedding_size in line 9
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- You need to set your data set folder in line 14
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- Then change the user number and POI number to fit the given data set in line 10 and line 11
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- You can also set the learning_rate_value to be a different value in line 17
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You can use your own data sets in the source code