Music recommendation and discovery revisited
Music recommendation and discovery revisited
复制标题
重新审视音乐推荐和发现
DOI：
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
Paul Lamere
中科院分区：
文献类型：
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作者：
Òscar Celma;Paul Lamere
音乐世界正在迅速变化。现在，我们只需点击几下鼠标，就可以收听几乎任何曾经录制过的歌曲。这种轻松获取几乎无穷无尽的音乐的方式正在改变我们探索、发现、分享和体验音乐的方式。
随着在线音乐世界的发展，音乐推荐和发现工具成为音乐听众参与音乐的越来越重要的方式。 Last.fm、iTunes Genius 和 Pandora 等商业推荐器已经取得了商业和评论界的成功。但这些系统的实际效果如何？这些建议有多好？这些推荐者的“长尾”能到达多远？
在本教程中，我们将了解当前音乐推荐和发现领域的最新技术。我们研究当前的商业和研究系统，重点关注各种推荐策略的优点和缺点。我们研究了构建音乐推荐器的一些挑战，并探索了一些用于改进未来音乐推荐和发现系统的新颖技术。
The world of music is changing rapidly. We are now just a few clicks away from being able to listen to nearly any song that has ever been recorded. This easy access to a nearly endless supply of music is changing how we explore, discover, share and experience music.
As the world of online music grows, music recommendation and discovery tools become an increasingly important way for music listeners to engage with music. Commercial recommenders such as Last.fm, iTunes Genius and Pandora have enjoyed commercial and critical success. But how well do these systems really work? How good are the recommendations? How far into the "long tail" do these recommenders reach?
In this tutorial we look at the current state-of-the-art in music recommendation and discovery. We examine current commercial and research systems, focusing on the advantages and the disadvantages of the various recommendation strategies. We look at some of the challenges in building music recommenders and we explore some of the novel techniques that are being used to improve future music recommendation and discovery systems.