2594538
doi
10.5281/zenodo.2594538
oai:zenodo.org:2594538
user-mir
Eva Zangerle
University of Innsbruck, Austria
Asmita Poddar
National University of Singapore
Yi-Hsuan Yang
Academia Sinica, Taiwan
Asmita Poddar
National University of Singapore
Yi-Hsuan Yang
Academia Sinica, Taiwan
#nowplaying-rs
Eva Zangerle
University of Innsbruck, Austria
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
context
recommender system
music
<p>The nowplaying-rs dataset features context- and content features of listening events. It contains 11.6 million music listening events of 139K users and 346K tracks collected from Twitter. The dataset comes with a rich set of item content features and user context features, as well as timestamps of the listening events. 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 a listening event.</p>
<p>The dataset contains three files:</p>
<ul>
<li>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 </li>
<li>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).</li>
<li>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: <hashtag, vader_min, vader_max, vader_sum,vader_avg, afinn_min, afinn_max, afinn_sum, afinn_avg, ol_min, ol_max, ol_sum, ol_avg, ss_min, ss_max, ss_sum, ss_avg >, where we abbreviate all scores gathered over the Opinion Lexicon with the prefix 'ol'. Similarly, 'ss' stands for SentiStrength. </li>
</ul>
<p>Please note that user_track_hashtag_timestamp.csv and context_content_features.csv partly provide the same features. We deliberately chose to do so to be able to provide useable files that do not have to be matched and joined with each other to perform e.g., simple recommendation tasks.</p>
<p>Please also find the training and test-splits for the dataset in this repo. Also, Asmita provides prototypical implementations of a context-aware recommender system based on the dataset at https://github.com/asmitapoddar/nowplaying-RS-Music-Reco-FM.</p>
<p><br>
If you make use of this dataset, please cite the following paper where we describe and experiment with the dataset:</p>
<p>@inproceedings{smc18,<br>
title = {#nowplaying-RS: A New Benchmark Dataset for Building Context-Aware Music Recommender Systems},<br>
author = {Asmita Poddar and Eva Zangerle and Yi-Hsuan Yang},<br>
url = {http://mac.citi.sinica.edu.tw/~yang/pub/poddar18smc.pdf},<br>
year = {2018},<br>
date = {2018-07-04},<br>
booktitle = {Proceedings of the 15th Sound & Music Computing Conference},<br>
address = {Limassol, Cyprus},<br>
note = {code at https://github.com/asmitapoddar/nowplaying-RS-Music-Reco-FM},<br>
tppubtype = {inproceedings}<br>
}</p>
Zenodo
2019-03-15
info:eu-repo/semantics/other
2594537
user-mir
1579893915.619981
1329064666
md5:fd14f7d94d432bbbdee31833da8babbf
https://zenodo.org/records/2594538/files/nowplayingrs.zip
public
10.5281/zenodo.2594537
isVersionOf
doi