FPMC
Python/Cython implementation of the: "Factorizing Personalized Markov Chains for Next-Basket Recommendation" paper.
Notes
Only works for baskets of size=1. Shoulde be easy to change to other sizes. Our datasets only have these sized baskets.
Dependencies for library
- Cython
- Numpy
- Pandas
How to install
Clone the repo
::
$ git clone https://github.com/flaviovdf/fpmc.git
Make sure you have cython and numpy. If not run as root (or use your distros package manager)
::
$ pip install numpy
::
$ pip install Cython
Install
::
$ python setup.py install
Run the main script or the cross_val script:
$ python main.py data_file num_latent_factors model.h5
This will read the data_file, decompose with num_latent_factors and save the model under the filename model.h5
The model is a pandas HDFStore. Just read-it with:
::
import pandas as pd
pd.HDFStore('model.h5')
The keys of this store have the output matrices described in the paper.
Input Format
The input file should have this format:
dt user from to
That is, a tab separated file where the first column is the amount of time the user
spent on from
before going to to
. The second column is the user id, the third
is the from
object, whereas the fourth is the destination to
object. I used
this input on other repositores, thus the main reason I kept it here.
This code will ignore the first column, so you can just use any float.
References
.. [1] Rendle, S. and Freudenthaler, C. and Schmidt-Thieme, L. "Factorizing Personalized Markov Chains for Next-Basket Recommendation" - WWW 2010