prismheart / STSP

Model proposed in 'A Win-Win Solution of Next POI Recommendation for Users-Businesses with Uncertain Check-ins'

STSP

This repository is the implementation of STSP

STSP is a model proposed in 'A Win-Win Solution of Next POI Recommendation for Users-Businesses with Uncertain Check-ins'. STSP is a novel framework, equipped with category- and location-aware encoders, which is designed to achieve next category and POI prediction with uncertain check-ins by fusing rich context features.

Files in the folder

• data/
  • mall_Info_CAL.csv: raw mall information of Calgary;
  • CAL_checkin.csv: raw checkin information of Calgary;
  • CAL_checkin_reindexed.csv: reindexed checkin information of Calgary;
• category result/
  • CAL
    • user_rep_CAL: user embedding folder of category encoder module, there are many .npy files;
    • L2_id_mapping_CAL.csv: category id mapping file;
    • reindex_data_CAL.csv: reindexed and filtered checkin file;
    • result_CAL.txt: the original category recommendation result of category encoder module;
    • train_CAL.txt: the original train data of category encoder;
• main.py: main file;
• data_preprocess.py: data preprocess file;
• category_encoder.py: category encoder module;
• POI_encoder.py: POI encoder module.

Required packages

The code has been tested running under Python 3.6.9, with the following packages installed (along with their dependencies):

• tensorflow==2.0.0
• numpy==1.17.3
• pandas=0.25.3
• keras==2.3.1

Running the code

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