A music recommendation system based on personal preference analysis

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Scopus citations

Abstract

In this paper, we propose a music recommendation system based on user preference analysis. The system builds music models using Hidden Markov Models with Mel Frequency Cepstral Coefficients, which are features of sound wave. Each song is modeled with an HMM and the similarity measure between songs are defined based on the models. With the similarity measure, the songs the user listened to in the past are grouped and analyzed. The system recommends pieces of music to the user based on the result of the analysis. We evaluate our system with virtual users who have various preferences, and observe which recommendation lists the system generates. In most cases, the system recommends the pieces of music which are close to user's preference.

Original languageEnglish
Title of host publication1st International Conference on the Applications of Digital Information and Web Technologies, ICADIWT 2008
Pages102-106
Number of pages5
DOIs
StatePublished - 2008
Event1st International Conference on the Applications of Digital Information and Web Technologies, ICADIWT 2008 - Ostrava, Czech Republic
Duration: 4 Aug 20086 Aug 2008

Publication series

Name1st International Conference on the Applications of Digital Information and Web Technologies, ICADIWT 2008

Conference

Conference1st International Conference on the Applications of Digital Information and Web Technologies, ICADIWT 2008
Country/TerritoryCzech Republic
CityOstrava
Period4/08/086/08/08

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