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CONTENT-BASED FILTERING FOR MUSIC RECOMMENDATION BASED ON UBIQUITOUS COMPUTING

机译:基于内容的音乐推荐基于普遍存算的滤波

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In music search and recommendation methods used in the present time, a general filtering method that obtains a result by inquiring music information and recommends a music list using users' profiles is used. However, this filtering method presents a certain difficulty to obtain users' information according to their circumstances because it only considers users' static information, such as personal information. In order to solve this problem, this paper defines a type of context information used in music recommendations and develops a new filtering method based on statistics by applying it to a content-based filtering method. In addition, a recommendation system using a content-based filtering method that was implemented by a ubiquitous computing technology was used to support service mobility and distribution processes. Based on the results of the performance evaluation of the system used in this study, it significantly increases not only the satisfaction for the music selection, but also the quality of services.
机译:在本时间使用的音乐搜索和推荐方法中,使用通过查询音乐信息获得结果的一般滤波方法,并建议使用用户的配置文件来获取音乐列表。然而,这种过滤方法呈现了根据其情况获得用户信息的一定困难,因为它仅考虑用户的静态信息,例如个人信息。为了解决这个问题,本文通过将基于内容的筛选方法应用于基于内容的过滤方法,定义了音乐推荐中使用的一种上下文信息,并通过将基于统计数据开发新的过滤方法。另外,使用普遍存在的计算技术实现的基于内容的过滤方法的推荐系统用于支持服务移动性和分发过程。基于本研究中使用的系统的性能评估的结果,不仅对音乐选择的满意度显着增加,而且显着增加了服务质量。

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