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Code repository of our RecSys 2023 paper "Of Spiky SVDs and Music Recommendation" - D. Afchar, R. Hennequin, V. Guigue (2023)

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RecSys 2023 -- Spiky SVD

Code for our paper "Of Spiky SVDs and Music Recommendation" by D. Afchar, R. Hennequin and V. Guigue, that will be presented at RecSys 2023 (September 18-22nd, 2023).

Dependencies

Our experiments run in Python and rely on a relatively light and standard toolkit in machine learning. Specifically, the pip package versions we have used to run our experiments are as follows:

numpy==1.21.5
scikit-learn==1.0.2
scipy==1.7.3
pandas==1.3.5

Experiments

  1. Data is prepared using ppmi.sql;
  2. SVDs are computed using Scikit-learn's randomized_svd(), our precise script is provided in compute_rsvd.py;
  3. Figure 1's embedding scatterplots are obtained using visualization.py and may be used to have a fine-grained view on the spiking effect;
  4. Table 1 may be reproduced using spk_computation.py;
  5. Stability plots are obtained using stability.py.

Data

We report to each link provided in the paper to get the raw datasets.

To ease reproducibility, we added the precomputed SVDs of the AoTM-2011, Cornell-Yes, LastFM-360K, Movielens-25M datasets. We cannot upload the precomputed SVDs of the two remaining datasets on GitHub due to the upload size limit (each .npy file is above 1GB). We will push said computed decompositions on Zenodo soon.

If not straightforward, scripts for selecting data from each dataset and converting it to a binary matrix are provided in the folder data_extraction.

We report some stats on each dataset:

Dataset #items type
AoTM-2011 27.241 track-playlist
Cornell 44.442 track-playlist
LastFM 94.272 artist-user
Movielens 42.490 movie-user
Spotify 1.261.730 track-playlist
Deezer 1.438.603 track-playlist

The number of considered items may be lower than found in the original datasets due to the filtering of items with too few interactions (as described in ppmi.sql).

Contact

[email protected]

About

Code repository of our RecSys 2023 paper "Of Spiky SVDs and Music Recommendation" - D. Afchar, R. Hennequin, V. Guigue (2023)

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