Culture-Aware Music Recommendation Dataset

LFM-1b dataset extended by acoustic track features and cultural cues describing users

This dataset is based on the LFM-1b dataset (cf. http://www.cp.jku.at/datasets/LFM-1b/), however, adds acoustic features describing the tracks to the original dataset as well as cultural aspects describing users (taken from Hofstede's six dimension model and the World Happiness Report) on the country-level.

For the creation of the dataset, we extract all users for which the original dataset contains country information for. We extract the listening events of these users and match the tracks against the Spotify API to subsequently retrieve the acoustic features of these tracks (cf. [Spotify Audio Feature Description](https://developer.spotify.com/documentation/web-api/reference/object-model/#audio-features-object)). The final dataset contains only events of users with country information and tracks with acoustic features, which can be matched with the country-level data of the World Happiness Report and Hofstede's cultural dimensions to add cultural and socio-economic aspects for users.

This new dataset contains

• 55,190 users
• 3,471,884 tracks including acoustic features
• 351,469,333 listening events of those users for tracks we have obtained acoustic features for
• Hofstede's cultural dimensions for 47 countries
• World Happiness Report (WHR) data for 164 countries

Files
All files are tab-separated, with no quoting of strings. The dataset contains the following files, whose content we describe in more detail in the following parts.

* acoustic_features_lfm_id.tsv: acoustic features for all tracks in the dataset, identified by their LFM track identifier
* events.tsv: listening events for all users
* hofstede.tsv: Hofstede's cultural dimensions
* users.tsv: user metadata
* world_happiness_report_2018.tsv: World Happiness Report data

For further information on the contents of these files, please cf. the Readme file.

Please cite the following paper when using the dataset:
Zangerle, E., Pichl, M. and Schedl, M., 2020. User Models for Culture-Aware Music Recommendation: Fusing Acoustic and Cultural Cues. Transactions of the International Society for Music Information Retrieval, 3(1), pp.1–16. DOI: http://doi.org/10.5334/tismir.37