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结合音乐内容和协同过滤方法之音乐推荐

【中文摘要】:伴随音乐讯号处理的进步以及网路的演进,音乐除了实体唱片与广播等传播方式之外,更有网路串流和行动装置等多元化之聆听方式,在音乐的推荐上除了透过歌曲的音讯分析外,亦有利用音乐的类型和使用者拨放纪录等额外之资讯进行演算,在现行音乐库不断快速增加的情况下,协助使用者找到可能喜欢的歌曲也是现行的主流音乐服务之一。
在网路串流音乐环境中,本研究将聆听纪录视为使用者的偏好习惯,并将之转换为使用者对该音乐的评分,将之作为推荐基础,进

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行演算,并依照现有的音乐资讯撷取领域之推

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荐系统的几个要素:音讯、音乐标籤、协同式方法,分别实作音乐内容、协同式处理方法,在音乐内容上尝试将音讯和音乐标籤进行筛选以及分类,并提出结合分类属性的改良方法;在协同式推荐方面则採用最近邻居方法和矩阵分解,并带入门阀值和权重值进行调整,透过这两种不同类型的推荐系统方法,找出使用者最感兴趣之音乐项目。对照过去研究所提出的推荐方法,上述两种之音乐推荐之改良方法皆得出较高的精确度。
【英文摘要】:With the music signal processing and network development, people can listen to music not only CDs, radio but also mobile devices, even more, online streaming.
Music recommendation systems use audio signals as classification features to predict user prefferences.In the recent years, music metadata such as music genre, music tags, and playlists can be treated as attribute to build recommendation models. The music database is growing explosively so that it is hard for users to find exactly what they want. Nowadays, there are many music services have recommendation systems for their users to find out this potential ‘songs’ quickly.
In online streaming environment, this research considers user playlists as user preferences, and converts playcounts to user preference rates. By this step, rates play

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a role in the classification and collaborative filtering methods. In music information retrieval field, content-based(audio, music tags) and collaborative approach are well known and frequently implemented in music services. In this research, content-based approach uses music signals and tag as classification features, and tries to combine both attributes into classification model; on the other hand, collaborative approach, uses k-nearest neighborhood method and matrix decomposition by tuning thresholds and vector weights. Both of these approachs are focusing on music items which user inetersted. According to the experement, approached methods accuracies are higher than traditional ones.
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  • 来源:中山大学;作者:梁展易
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