融合Fisher判别分析与波动序列的音乐推荐方法

TP391; 现有的音乐推荐方法多是采用不同的历史偏好相关性度量方法直接为用户生成推荐音乐列表,而不考虑用户历史喜好音乐行为所体现出的用户兴趣的波动性,影响了推荐音乐的准确率.针对这个问题,提出了一种融合Fisher线性判别分析与波动序列的音乐行为偏好获取方法.首先获取音乐的社会化标签与音频特征,采用Fisher线性判别分析对两类样本数据进行特征融合,通过投影变换并引入Fisher判别准则,获取具有最大类间离散度,最小类内离散度的音乐特征分类方向.然后结合用户的历史喜好音乐获取音乐类型基点、类型波动幅度,再以音乐类型基点为中心,以类型波动幅度为半径获取用户的喜好音乐类型,并据此为用户生成推荐音...

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Published in计算机科学与探索 Vol. 11; no. 8; pp. 1314 - 1323
Main Authors 薛董敏, 赵志华
Format Journal Article
LanguageChinese
Published 山西水利职业技术学院 信息工程系,山西 运城,044000 2017
Subjects
音乐推荐系统
Fisher线性判别分析
music recom-mender systems
音乐类型基点
Fisher linear discriminant analysis
music type base point
volatility sequence
social tags
波动序列
社会化标签
Online AccessGet full text
ISSN1673-9418
DOI10.3778/j.issn.1673-9418.1604064

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Abstract TP391; 现有的音乐推荐方法多是采用不同的历史偏好相关性度量方法直接为用户生成推荐音乐列表,而不考虑用户历史喜好音乐行为所体现出的用户兴趣的波动性,影响了推荐音乐的准确率.针对这个问题,提出了一种融合Fisher线性判别分析与波动序列的音乐行为偏好获取方法.首先获取音乐的社会化标签与音频特征,采用Fisher线性判别分析对两类样本数据进行特征融合,通过投影变换并引入Fisher判别准则,获取具有最大类间离散度,最小类内离散度的音乐特征分类方向.然后结合用户的历史喜好音乐获取音乐类型基点、类型波动幅度,再以音乐类型基点为中心,以类型波动幅度为半径获取用户的喜好音乐类型,并据此为用户生成推荐音乐列表.在真实数据集LFM上的仿真实验结果表明,所提出方法能够取得更好的P@R值与覆盖率,提升了音乐推荐精度与推荐质量.
AbstractList TP391; 现有的音乐推荐方法多是采用不同的历史偏好相关性度量方法直接为用户生成推荐音乐列表,而不考虑用户历史喜好音乐行为所体现出的用户兴趣的波动性,影响了推荐音乐的准确率.针对这个问题,提出了一种融合Fisher线性判别分析与波动序列的音乐行为偏好获取方法.首先获取音乐的社会化标签与音频特征,采用Fisher线性判别分析对两类样本数据进行特征融合,通过投影变换并引入Fisher判别准则,获取具有最大类间离散度,最小类内离散度的音乐特征分类方向.然后结合用户的历史喜好音乐获取音乐类型基点、类型波动幅度,再以音乐类型基点为中心,以类型波动幅度为半径获取用户的喜好音乐类型,并据此为用户生成推荐音乐列表.在真实数据集LFM上的仿真实验结果表明,所提出方法能够取得更好的P@R值与覆盖率,提升了音乐推荐精度与推荐质量.
Abstract_FL The existing music recommendation methods often use similarity or correlation to generate recommended music list, those methods don ' t consider the volatility of users ' interest reflected by the historical music behavior, which influences the recommendation accuracy. To solve this problem, this paper proposes a music recommendation method based on Fisher linear discriminant analysis and volatility sequence. In the beginning, this method obtains the social tags and audio features of music to compute the projection direction which has the minimum within-class scatter and maximum between-class scatter, by using projection transformation and Fisher discriminant criterion. This projection direction is also the best direction of classification. Then it takes music type base point as center, type vol-atility range as radius to acquire users ' preferred music type, and based on which to generate the recommendation list. This paper presents the empirical experiments in a real data set LFM, the results show that the proposed method can achieve better P@R and coverage rate, which means it efficiently improves recommendation accuracy and quality.
Author 薛董敏
赵志华
AuthorAffiliation 山西水利职业技术学院 信息工程系,山西 运城,044000
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Author_FL XUE Dongmin
ZHAO Zhihua
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Keywords 音乐推荐系统
Fisher线性判别分析
music recom-mender systems
音乐类型基点
Fisher linear discriminant analysis
music type base point
volatility sequence
social tags
波动序列
社会化标签
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Title 融合Fisher判别分析与波动序列的音乐推荐方法
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