Code for the article: O. Gouvert, T. Oberlin, C. Févotte "Ordinal Non-negative Matrix Factorization for Recommendation" arXiv: https://arxiv.org/abs/2006.01034
Implemented in Python 2.7.
This folder contains:
- data: contains MovieLens (ML) and Taste Profile (TPS) datasets and preprocessing.
- function:
- preprocess_data.py used to split datasets into train set + test set.
- rec_eval used to calculate evaluation metrics.
- model:
- OrdNMF implementation described in the paper.
- dcPF implementation as described in Gouvert et al. (2019) (PF as a limit case)
- script: contains the experiments for both datasets
- train: training models
- score: evaluating on test set
- ppc: predictive posterior checks