Exposure Matrix Factorization: modeling user exposure in recommendation
Jupyter Notebook Python
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Dawen Liang
Dawen Liang minor refactor
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README.md Update README.md Feb 14, 2016



This repository contains the source code to reproduce all the experimental results as described in the paper "Modeling User Exposure in Recommendation" (WWW'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.


We also used the arXiv and Mendeley dataset in the paper. However, these datasets are not publicly available. With Taste Profile Subset and Gowalla, we can still cover all the different variations of the model presented in the paper.

We used the weighted matrix factorization (WMF) implementation in content_wmf repository.


See example notebooks in src/.