Music Recommendation Based on Label Correlation
The Web is becoming the largest source of digital music, and users often find themselves exposed to a huge collection of items. How to effectively help users explore through massive music items creates a significant challenge that must be properly addressed in the era of E-Commerce. For this purpose, a number of music recommendation systems have been proposed and implemented, which can identify music items that are likely to be appealing to a specific user. This paper presents a hybrid music recommendation system based on the labels associated with each music album, which also explicitly takes into account the correlation among labels. Experimental results on a real-world sales dataset show that our approach can achieve a clear advantage in terms of precision and recall over traditional methods in which labels are treated as independent keywords.
This work was supported by the National Natural Science Foundation of China (No. 60905030). The authors are also grateful to the LP album store owner for providing the sales dataset.
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