A personalized music recommendation system with a time-weighted clustering

Taebok Yoon, Seunghoon Lee, KwangHo Yoon, Dong-Moon Kim, Jee-Hyong Lee
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引用次数: 10

Abstract

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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基于时间加权聚类的个性化音乐推荐系统
我们提出了一个提供个性化服务的音乐推荐系统。系统保留用户的收听列表,并对其进行分析,选择与用户偏好相似的音乐。为了进行分析,该系统从音乐的声波和用户听音乐的时间中提取属性。基于这些属性,将音乐片段映射到属性空间中的一个点,并将时间转换为该点的权值。用户听音乐的时间越近,权重增加的越多。我们将K-means聚类算法应用于加权点。改进K-means算法,使聚类数量动态变化。通过使用我们的K-means聚类算法,我们可以推荐接近用户偏好的音乐片段,即使他喜欢几个流派。我们还会考虑音乐发行的时间。在进行推荐时,系统会根据用户的喜好选择与当前发布的音乐作品相近的曲目。我们用一百首乐曲做实验。在本文中,我们提出并评估了推荐系统的算法。
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