This paper develops a music recommendation system that automates the downloading of songs into a mobile digital audio device. The system tailors the composition of the songs to the preferences of individuals based on past behaviors. By assuming that an individual will listen longer to a song that provides a higher utility, we describe and predict individual listening behavior using a lognormal hazard function. Our recommendation system is the first to accomplish this and there is no viable alternative. Yet, our proposed approach provides an improvement over naïve methods that could be used for product recommendations. Our system has a number of distinct features…
Author: Chung, Tuck Siong
Source: University of Maryland
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