#nowplaying-RS: A New Benchmark Dataset for Building Context-Aware Music Recommender Systems

Music recommender systems can offer users personalized and contextualized recommendation and are therefore important for music information retrieval. An increasing number of datasets have been compiled to facilitate research on different topics, such as content-based, context-based or next-song recommendation. However, these topics are usually addressed separately using different datasets, due to the lack of a unified dataset that contains a large variety of feature types such as item features, user contexts, and timestamps. To address this issue, we propose a large-scale benchmark dataset called #nowplaying-RS, which contains 11.6 million music listening events (LEs) of 139K users and 346K tracks collected from Twitter. The dataset comes with a rich set of item content features and user context features, and the timestamps of the LEs. Moreover, some of the user context features imply the cultural origin of the users, and some others—like hashtags—give clues to the emotional state of a user underlying an LE. In this paper, we provide some statistics to give insight into the dataset, and some directions in which the dataset can be used for making music recommendation. We also provide standardized training and test sets for experimentation, and some baseline results obtained by using factorization machines.

The dataset contains three files:

  • user_track_hashtag_timestamp.csv contains basic information about each listening event. For each listening event, we provide an id, the user_id, track_id, hashtag, created_at 
  • context_content_features.csv: contains all context and content features. For each listening event, we provide the id of the event, user_id, track_id, artist_id, content features regarding the track mentioned in the event (instrumentalness, liveness, speechiness, danceability, valence, loudness, tempo, acousticness, energy, mode, key) and context features regarding the listening event (coordinates (as geoJSON), place (as geoJSON), geo (as geoJSON), tweet_language, created_at, user_lang, time_zone, entities contained in the tweet).
  • sentiment_values.csv contains sentiment information for hashtags. It contains the hashtag itself and the sentiment values gathered via four different sentiment dictionaries: AFINN, Opinion Lexicon, Sentistrength Lexicon and vader. For each of these dictionaries we list the minimum, maximum, sum and average of all sentiments of the tokens of the hashtag (if available, else we list empty values). However, as most hashtags only consist of a single token, these values are equal in most cases. Please note that the lexica are rather diverse and therefore, are able to resolve very different terms against a score. Hence, the resulting csv is rather sparse. The file contains the following comma-separated values: , where we abbreviate all scores gathered over the Opinion Lexicon with the prefix 'ol'. Similarly, 'ss' stands for SentiStrength. 

Please also find the training and test-splits for the dataset in this repo. Also, prototypical implementations of a context-aware recommender system based on the dataset can be found at  https://github.com/asmitapoddar/nowplaying-RS-Music-Reco-FM.

If you make use of this dataset, please cite the following paper where we describe and experiment with the dataset:

@inproceedings{smc18,
title = {#nowplaying-RS: A New Benchmark Dataset for Building Context-Aware Music Recommender Systems},
author = {Asmita Poddar and Eva Zangerle and Yi-Hsuan Yang},
url = {http://mac.citi.sinica.edu.tw/~yang/pub/poddar18smc.pdf},
year = {2018},
date = {2018-07-04},
booktitle = {Proceedings of the 15th Sound & Music Computing Conference},
address = {Limassol, Cyprus},
note = {code at https://github.com/asmitapoddar/nowplaying-RS-Music-Reco-FM},
tppubtype = {inproceedings}
}