C4.5: programs for machine learning
C4.5: programs for machine learning
A Music Recommendation System Based on Annotations about Listeners' Preferences and Situations
AXMEDIS '05 Proceedings of the First International Conference on Automated Production of Cross Media Content for Multi-Channel Distribution
A music recommendation system based on music and user grouping
Journal of Intelligent Information Systems - Special issue: Intelligent multimedia applications
Automatic playlist generation based on tracking user's listening habits
Multimedia Tools and Applications
Effectiveness of note duration information for music retrieval
DASFAA'05 Proceedings of the 10th international conference on Database Systems for Advanced Applications
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A music hobbyist listens to different types of music at different times of the day. Thus, an automatic music recommendation that can adjust to the hobbyist's daily activities on this basis is necessary in order to generate the appropriate music to suit the user's current activity, whether it is working or driving. Although existing research has introduced various music recommendation systems, there is yet a system that generates the music recommendation based on time. Hence, in this paper, we present a music recommendation system, which provides an automatic and personalized music playing service based on the time parameter and user's interesting. This system represents the characteristics of music from features extracted out of the music's symbolic form. The user's music rating history and the associated time stamps in the user's profile constitute the training data of the intelligent system. The effectiveness and efficiency of artificial neural network and decision tree are investigated as the kernels of the system. A series of experiments have been carried out to demonstrate the performance of this system.