Content-based music filtering system with editable user profile
Content-based music filtering system with editable user profile
复制标题
基于内容的音乐过滤系统，具有可编辑的用户配置文件
DOI：
10.1145/1141277.1141526
复制
发表时间：
2006
期刊：
影响因子：
--
通讯作者：
S. Nishida
中科院分区：
文献类型：
--
作者：
Y. Hijikata;Kazuhiro Iwahama;S. Nishida
从海量信息中向用户推荐合适信息的信息过滤系统正在普及。信息过滤的一种方法是基于内容的过滤，它将用户概要文件与内容模型进行比较。许多系统使用基于内容的过滤来处理文本数据，而很少有系统处理音乐数据。本文提出了一种基于内容的音乐数据过滤系统。与其他过滤方法相比，决策树可以消除与用户偏好无关的噪声特征，并允许用户编辑学习到的用户配置文件。我们用真实的音乐数据和用户进行了实验，对比其他过滤方法，验证了我们的系统的有效性。
Information filtering systems, which recommend appropriate information to users from enormous amount of information, are becoming popular. One method of information filtering is content-based filtering that compares a user profile with a content model. Many systems using content-based filtering deal with text data, and few systems deal with music data. We propose a content-based filtering system for music data by using a decision tree. Compared with other filtering methods, a decision tree can eliminate noise features, which are not related to the user's preference, and can allow the user to edit the learned user profile. We conduct an experiment by using real music data and users to validate the effectiveness of our system compared with other filtering methods.