- Anaconda 4.X (Python 3.5+)
- Pympler
- NumPy
- SciPy
- BLAS
- Pandas
- Theano
- Tensorflow
- Suitable datasets (https://www.dropbox.com/sh/n281js5mgsvao6s/AADQbYxSFVPCun5DfwtsSxeda?dl=0)
- Download and install Anaconda (https://www.continuum.io/).
- NumPy, SciPy, BLAS, Pandas should automatically be included.
- Install build essentials
- apt-get install build-essential
- On Windows the installation of mingw with Anaconda should work.
- conda install mingw
- Install Theano, Tensorflow and additional packages
- conda install theano tensorflow pympler
- Unzip any dataset file to the data folder, i.e., rsc15-clicks.dat will then be in the folder data/rsc15/raw
- Open the script run_preprocessing*.py to configure the preprocessing method and parameters
- run_preprocessing_rsc15.py is for the RecSys challenge dataset.
- run_preprocessing_tmall.py is for the TMall logs.
- run_preprocessing_retailrocket.py is for the Retailrocket competition dataset.
- run_preprocessing_clef.py is for the Plista challenge dataset.
- run_preprocessing_music.py is for all music datasets (configuration of the input and output path inside the file).
- Run the script
- You must have run the preprocessing scripts previously
- Open and edit one of the run_test*.py scripts
- run_test.py evaluates predictions for single split in terms of just the next item (HR@X and MRR@X)
- run_test_pr.py evaluates predictions for single split in terms of all remaining items in the session (P@X, R@X, and MAP@X)
- run_test_window.py evaluates predictions for sliding window split in terms of the next item (HR@X and MRR@X)
- run_test_buys.py evaluates buy events in the sessions (only for the rsc15 dataset).The script run_preprocessing.py must have been executed with method "buys" before.
- The usage of all algorithms is exemplarily shown in the script.
- Run the script
- Results and run times will be displayed and saved to the results folder as configured