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A Personalized Music  Recommendation System with a Time-weighted Clustering

byIISLab 2012.03.19 06:48
저자 Taebok Yoon, Seunghoon Lee, KwangHo Yoon, Dong-Moon Kim, and Jee-Hyong Lee 
학회명 International IEEE Conference on Intelligent Systems 
학회명 (약자) IS 2008 
pp. 48-52 
학회시작일 2008-09-06 
학회종료일 2008-09-08 
비고  
We propose a music recommendation system which provides personalized services. The system keeps a userpsilas listening list and analyzes it to select pieces of music similar to the userpsilas preference. For analysis, the system extracts properties from the sound wave of music and the time when the user listens to music. Based on the properties, a pieces of music is mapped into a point in the property space and the time is converted into the weight of the point. The more recently the user listens to the music, the more the weight increases. We apply the K-means clustering algorithm to the weighted points. The K-means algorithm is modified so that the number of clusters are dynamically changed. By using our K-means clustering algorithm, we can recommend pieces of music which are close to userpsilas preference even though he likes several genres. We also consider the time when music is released. When recommending, the system selects pieces of music which is close to and released contemporarily with the userpsilas preference. We perform experiments with one hundred pieces of music. In this paper we present and evaluate algorithms to recommend system.
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