This repository contains the source code to reproduce the experimental results as described in the paper "Factorization Meets the Item Embedding: Regularizing Matrix Factorization with Item Co-occurrence" (RecSys'16).
The python module dependencies are:
- numpy/scipy
- scikit.learn
- joblib
- bottleneck
- pandas (needed to run the example for data preprocessing)
Note: The code is mostly written for Python 2.7. For Python 3.x, it is still usable with minor modification. If you run into any problem with Python 3.x, feel free to contact me and I will try to get back to you with a helpful solution.
- Taste Profile Subset: the pre-processing is done following this notebook.
- MovieLens-20M
We adapted the weighted matrix factorization (WMF) implementation from content_wmf repository.
See example notebooks in src/.