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首页> 外文会议>International Conference on Education, Management, Arts, Economics and Social Science >A Personalized Music Recommendation Algorithm Based on User Implicit Feedback and Weighted Socialized Tag Content Filtering
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A Personalized Music Recommendation Algorithm Based on User Implicit Feedback and Weighted Socialized Tag Content Filtering

机译：基于用户隐式反馈和加权社交标记内容过​​滤的个性化音乐推荐算法

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摘要

The popularity of the Internet and the electronization of music resources make it easier for people to get music what they like. However, facing abundant resources, it is difficult for people to find their favorite music accurately and timely. Personalized music recommendation algorithms play an increasingly important role in online music service systems. Traditional feedback method requires the user to give feedback explicitly, which not only increases information collection cost but also gives the user an additional burden. This paper proposes a content filtering recommendation method based on user implicit feedback and weighted socialized tags, and designs experiment to compare our recommendation method with fuzzy theory method. The experimental results show this method is more accurate than the fuzzy theory method in recommending music to the user.
机译：互联网的普及和音乐资源的电子化使人们更容易获得音乐他们喜欢的东西。然而，面对丰富的资源，人们很难准确和及时找到他们最喜欢的音乐。个性化音乐推荐算法在在线音乐服务系统中起着越来越重要的作用。传统的反馈方法要求用户明确地提供反馈，这不仅提高了信息收集成本，而且还使用户提供了额外的负担。本文提出了一种基于用户隐含反馈和加权社会化标签的内容过滤推荐方法，并设计实验，以比较我们用模糊理论方法的推荐方法。实验结果表明，该方法比模糊理论方法在推荐给用户的模糊理论方法更准确。

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