Department of Computer Science, Dankook University, 152 Jukjeon-ro Campus, Suji-gu, Yongin-si 16890, Republic of Korea.
Sensors (Basel). 2023 Jul 25;23(15):6670. doi: 10.3390/s23156670.
This paper proposes a recommendation system based on a hybrid learning approach for a personal deep sleep service, called the Customized Deep Sleep Recommender System (CDSRS). Sleep is one of the most important factors for human life in modern society. Optimal sleep contributes to increasing work efficiency and controlling overall well-being. Therefore, a sleep recommendation service is considered a necessary service for modern individuals. Accurate sleep analysis and data are required to provide such a personalized sleep service. However, given the variations in sleep patterns between individuals, there is currently no international standard for sleep. Additionally, service platforms face a cold start problem when dealing with new users. To address these challenges, this study utilizes K-means clustering analysis to define sleep patterns and employs a hybrid learning algorithm to evaluate recommendations by combining user-based and collaborative filtering methods. It also incorporates feedback top-N classification processing for user profile learning and recommendations. The behavior of the study model is as follows. Using personal information received through mobile devices and data, such as snoring, sleep time, movement, and noise collected through AI motion beds, we recommend sleep and receive user evaluations of recommended sleep. This assessment reconstructs the profile and, finally, makes recommendations using top-N classification. The experimental results were evaluated using two absolute error measurement methods: mean squared error (MSE) and mean absolute percentage error (MAPE). The research results regarding the hybrid learning methods show 13.2% fewer errors than collaborative filtering (CF) and 10.2% fewer errors than content-based filtering (CBF) on an MSE basis. According to the MAPE, the methods are 14.7% more accurate than the CF model and 9.2% better than the CBF model. These results demonstrate that CDSRS systems can provide more accurate recommendations and customized sleep services to users than CF, CBF, and combination models. As a result, CDSRS, a hybrid learning method, can better reflect a user's evaluation than traditional methods and can increase the accuracy of recommendations as the number of users increases.
本文提出了一种基于混合学习方法的个人深度睡眠服务推荐系统，称为定制深度睡眠推荐系统（CDSRS）。睡眠是现代社会人类生活最重要的因素之一。优质的睡眠有助于提高工作效率和控制整体健康。因此，睡眠推荐服务被认为是现代个人的必要服务。提供这种个性化的睡眠服务需要准确的睡眠分析和数据。然而，由于个体之间的睡眠模式存在差异，目前还没有国际公认的睡眠标准。此外，服务平台在处理新用户时面临冷启动问题。为了解决这些挑战，本研究利用 K-均值聚类分析来定义睡眠模式，并采用混合学习算法，通过结合基于用户和基于协作的过滤方法来评估推荐。它还结合了用户配置文件学习和推荐的反馈前 N 分类处理。研究模型的行为如下。使用通过移动设备接收的个人信息和数据，例如通过 AI 运动床收集的打鼾、睡眠时间、运动和噪音，我们推荐睡眠并接收用户对推荐睡眠的评价。该评估会重新构建配置文件，最后使用前 N 分类进行推荐。使用两种绝对误差测量方法（均方误差（MSE）和平均绝对百分比误差（MAPE））评估实验结果。基于 MSE 的混合学习方法的研究结果显示，与协作过滤（CF）相比，误差减少了 13.2%，与基于内容的过滤（CBF）相比，误差减少了 10.2%。根据 MAPE，该方法比 CF 模型准确 14.7%，比 CBF 模型好 9.2%。这些结果表明，CDSRS 系统可以为用户提供比 CF、CBF 和组合模型更准确的推荐和定制睡眠服务。因此，与传统方法相比，CDSRS 混合学习方法可以更好地反映用户的评价，并随着用户数量的增加提高推荐的准确性。
