Hubness as a Case of Technical Algorithmic Bias in Music Recommendation

A. Flexer, M. Dörfler, Jan Schlüter, Thomas Grill
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引用次数: 5

Abstract

This paper tries to bring the problem of technical algorithmic bias to the attention of the high-dimensional data mining community. A system suffering from algorithmic bias results in systematic unfair treatment of certain users or data, with technical algorithmic bias arising specifically from technical constraints. We illustrate this problem, which so far has been neglected in high-dimensional data mining, for a real world music recommendation system. Due to a problem of measuring distances in high dimensional spaces, songs closer to the center of all data are recommended over and over again, while songs far from the center are not recommended at all. We show that these so-called hub songs do not carry a specific semantic meaning and that deleting them from the data base promotes other songs to hub songs being recommended disturbingly often as a consequence. We argue that it is the ethical responsibility of data mining researchers to care about the fairness of their algorithms in high-dimensional spaces.
Hubness:音乐推荐中技术算法偏差的一个案例
本文试图引起高维数据挖掘界对技术算法偏差问题的重视。遭受算法偏见的系统导致对某些用户或数据的系统性不公平对待,技术算法偏见具体源于技术限制。我们用一个现实世界的音乐推荐系统来说明这个迄今为止在高维数据挖掘中被忽视的问题。由于在高维空间中测量距离的问题,靠近所有数据中心的歌曲被反复推荐,而远离中心的歌曲根本不被推荐。我们表明,这些所谓的中心歌曲没有特定的语义含义,从数据库中删除它们会促进其他歌曲成为中心歌曲,结果往往令人不安地被推荐。我们认为,关心高维空间中算法的公平性是数据挖掘研究人员的道德责任。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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