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Adaptive Music Recommendation Based on User Behavior in Time Slot


Ning-Han Liu, Szu-Wei Lai, Chien-Yi Chen, Shu-Ju Hsieh


Vol. 9  No. 2  pp. 219-227


The digital music is booming through rapid expansion of Internet. Users can listen to their favorite songs via web all the time. The electronic commercial leads to development of the recommendation system, which enhances the desire of music buying for customers. The online music recommendation system usually grabs the historical record from past listeners. With extensive data analysis or statistical means, the system recommends the popular music to others. However, users’ demand is far beyond that because their choice or change of listening behaviors is often affected by various factors, such as time or place. If the system only considers the type of favorite songs for users, it seems not a comprehensive service for personal recommendation system. So this research will add time scheduling to the music playlist, and combines classification technology of decision tree to suggest users the suit music more precisely. Eventually, the accuracy of recommend results achieved in our anticipate result after implementation and analysis.


Music recommend system, decision tree, temporal continuity, content-filtering, collaborative-filtering