Personalized Content-Based Music Retrieval by User-Filtering and Query-Refinement

Ja-Hwung Su, T. Hong, Jyun-Yu Li, Jung-Jui Su
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引用次数: 3

Abstract

In recent years, music is an important media because it can relax us in our daily life. Therefore, most people listen to music frequently and current music websites offer online listening services. However, because the semantic gap, it is not easy to effectively retrieve the user preferred music especially from a huge amount of music data. For this issue, this paper presents a personalized content-based music retrieval system that integrates techniques of user-filtering and query-refinement to achieve high quality of music retrieval. In terms of user-filtering, the new user interest can be inferred by the user similarities. In terms of query-refinement, the user interest can be guided to the potential search space by iterative feedbacks. The experimental results show the proposed method does improve the retrieval quality significantly.
基于用户过滤和查询细化的个性化内容音乐检索
近年来,音乐是一个重要的媒体,因为它可以放松我们在我们的日常生活。因此,大多数人经常听音乐,现在的音乐网站提供在线听音乐服务。然而,由于语义差距的存在,很难有效地从海量的音乐数据中检索到用户喜欢的音乐。针对这一问题,本文提出了一个个性化的基于内容的音乐检索系统,该系统集成了用户过滤和查询细化技术,以实现高质量的音乐检索。在用户过滤方面,可以通过用户相似度来推断新用户的兴趣。在查询细化方面,可以通过迭代反馈将用户兴趣引导到潜在的搜索空间。实验结果表明,该方法显著提高了检索质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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