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