MusicDress: A Heterogeneous Dataset for Comparing Music Recommender Systems

Johannes Schoder, H. M. Bücker, André Bötticher, Ronja M. Karmann
{"title":"MusicDress: A Heterogeneous Dataset for Comparing Music Recommender Systems","authors":"Johannes Schoder, H. M. Bücker, André Bötticher, Ronja M. Karmann","doi":"10.1109/SNAMS58071.2022.10062594","DOIUrl":null,"url":null,"abstract":"To compare different types of music recommender systems, datasets are necessary that offer a combination of diverse features. We propose MusicDress, a novel dataset covering four different elements of music: timbre, rhythm, melody, and harmony. The dataset extends to lyrics and user data by linking to publicly available data sources. It comprises features of 2,136 individual songs and enables the comparison of hybrid recommender systems that combine content-based, context-based, and collaborative filtering approaches.","PeriodicalId":371668,"journal":{"name":"2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SNAMS58071.2022.10062594","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

To compare different types of music recommender systems, datasets are necessary that offer a combination of diverse features. We propose MusicDress, a novel dataset covering four different elements of music: timbre, rhythm, melody, and harmony. The dataset extends to lyrics and user data by linking to publicly available data sources. It comprises features of 2,136 individual songs and enables the comparison of hybrid recommender systems that combine content-based, context-based, and collaborative filtering approaches.
MusicDress:一个用于比较音乐推荐系统的异构数据集
为了比较不同类型的音乐推荐系统,提供不同特征组合的数据集是必要的。我们提出MusicDress,这是一个新的数据集,涵盖了音乐的四个不同元素:音色、节奏、旋律和和声。该数据集通过链接到公开可用的数据源扩展到歌词和用户数据。它包含2136首独立歌曲的特征,并能够比较结合了基于内容、基于上下文和协作过滤方法的混合推荐系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信