Do Recommender Systems Promote Local Music? A Reproducibility Study Using Music Streaming Data - Dataset

Overview

This dataset supports the research paper "Do Recommender Systems Promote Local Music? A Reproducibility Study Using Music Streaming Data," authored by Deezer and CNRS researchers Kristina Matrosova, Lilian Marey, Guillaume Salha-Galvan, Thomas Louail, Olivier Bodini, and Manuel Moussallam as part of the RECORDS initiative (https://records.huma-num.fr/). The paper, accepted at the 18th ACM Recommender Systems Conference (RecSys 2024), explores how recommender algorithms influence the promotion of local music.

Data Description

.inter Files

The .inter files contain the listening histories of 10,000 Deezer users from Brazil (BR), France (FR), and Germany (DE) over a period of 1 months (March 2019). Each record includes user, item (track), and artist IDs. The DEEZER_GLOBAL.inter file is a combined dataset of these three countries.

All IDs have been hashed and reindexed.

Column names: user_id, item_id, artist_id (only for global file)

.csv Files

- user_country.csv: Links each user ID in the global .inter dataset to their country (BR, FR, or DE).
  Column names: user_id, country

- metadata_DEEZER Files: Match artist IDs with their countries using three different methods:
  - active: Artist’s country of activity
  - origin: Artist’s country of origin
  - musicbrainz: Country according to the MusicBrainz database (https://musicbrainz.org)

    Column names: item_id, country