Content-based music filtering system with editable user profile

Abstract

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.