If You Like Radiohead, You Might Like This Article

Author:

Celma Oscar,Lamere Paul

Abstract

With the recent dramatic transformations in the world of digital music, a music listener is now just a couple of clicks away from being able to listen to nearly any song that has ever been recorded. With so much music readily available, tools that help a user find new, interesting music that matches her taste become increasingly important. In this article we explore one such tool: music recommendation. We describe common music recommendation use cases such as finding new artists, finding others with similar listening taste, and generating interesting music playlists. We describe the various approaches currently being explored by practitioners to satisfy these use cases. Finally, we show how results of three different music recommendation technologies compare when applied to the task of finding similar artists to a seed artist.

Publisher

Association for the Advancement of Artificial Intelligence (AAAI)

Subject

Artificial Intelligence

Cited by 10 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Looking at the FAccTs: Exploring Music Industry Professionals' Perspectives on Music Streaming Services and Recommendations;Proceedings of the 2nd International Conference of the ACM Greek SIGCHI Chapter;2023-09-27

2. A Hybrid Recommender System for Improving Automatic Playlist Continuation;IEEE Transactions on Knowledge and Data Engineering;2019

3. The ‘Creative Listener:’ Internet, Music, and the Computer-Bodymind Somatechnic;Somatechnics and Popular Music in Digital Contexts;2019

4. Situation awareness for recommender systems;Electronic Commerce Research;2018-10-24

5. A Data-driven Approach to Identifying Music Listener Groups based on Users' Playrate Distributions of Listening Events;Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization;2017-07-09

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