Abstract
Music recommendation plays a key role in our daily lives as well as in the multimedia industry. This paper adapts the memory forgetting curve to model the human interest-forgetting curve for music recommendations based on the observation of recency effects in people's listening to music. Two music recommendation methods are proposed using this model with respect to the sequence-based and the IFC-based transition probabilities, respectively. We also bring forward a learning method to approximate the global optimal or personalized interest-forgetting speed(s). The experimental results show that our methods can significantly improve the accuracy in music recommendations. Meanwhile, the IFC-based method outperforms the sequence-based method when recommendation list is short at each time.
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