由於現代人生活型態改變，因而出現許多睡眠問題。失眠問題尤其嚴重，失眠症除了會出現難以入睡、睡不著、太早醒來等困擾，如處理不當更會造成憂鬱、肥胖和心血管疾病等風險增加。現今的治療多半還是採用藥物治療，使得病患出現藥物濫用、副作用等狀況，而在非藥物治療方面，聲音對於睡眠的研究或是治療已有一定的研究基礎，但由於聲音相當主觀的感受，怎麼樣的聲音才是適合怎樣人的睡眠始終無法得知，因而無法有效應用於失眠症上。現今人工智慧多元發展，較少透過實際的產品或服務針對失眠者進行實驗。另外隨著手機的應用相當廣泛，對於使用者的感受也需多加探討。
因此基於上述，本研究設計一款白噪音音樂串流推薦系統應用程式，並採用基於基於項目的協同過濾，推薦合適的白噪音給失眠患者，用以改善失眠者的睡眠品質。最終我們成功建置出該套系統原型，並對16位輕度失眠患者運用隨機對照法，進行五天的睡眠評估實驗，驗證該系統的有效性。使用無母數進行統計實驗組與對照組的深度睡眠平均分別為25.4%和21.7%，而快速動眼期的平均則為27.4%和23.4%，能有助於提升失眠者穩定的深層睡眠與快速動眼期。同時我們也藉由SUS系統使用性量表和半結構式訪談，對8位失眠者進行使用性評估，了解該系統之使用性與意願度，結果顯示該系統使用性評分為85分，屬於使用性良好狀態。最終研究結果表明，所提出的機器學習推薦白噪音方案，能夠幫助失眠患者在良好的使用體驗中，找到合適的白噪音，並且有助於改善他們的睡眠品質。
本研究的宗旨在於設計能夠改善失眠患者的白噪音推薦系統，協助改善失眠問題，最終本研究所帶來的成果，在未來可提供給日後聲音與睡眠、人機互動、機器學習等相關研究參考之用。

Many sleep problems have occurred due to changes in the modern lifestyle. Insomnia is relatively more serious than other sleep disorders. An insomnia not handled properly will increase the risk of depression, obesity, and cardiovascular diseases. Nowadays, most sleep therapies involve treatment with drugs, which cause side effects on some patients. For non-drug therapies, sound is used to assist sleep, and its research has a solid foundation. However, sound is quite subjective, and it is difficult to determine the sound suitable for each individual. Therefore, it cannot be effectively applied to insomnia. With the diversified development of artificial intelligence, experiments with insomnia are rarely conducted through actual products or services. Besides, as mobile phones' application is quite extensive, more discussion is needed for user experience.

Therefore, based on the above, this research designs a white noise streaming recommendation system application. It uses project-based collaborative filtering to recommend appropriate white noise to patients with insomnia to improve their sleep quality. In the end, we successfully built the system prototype. Then we used a randomized controlled method for 16 patients with mild insomnia to conduct a five-day sleep assessment experiment. It is used to verify the effectiveness of the system. We use nonparametric tests for statistics. The experimental group's average deep sleep and the control group were 25.4% and 21.7%, respectively, while the average of the REM period was 27.4% and 23.4%. This result indicates that it can help improve the stable deep sleep and REM period of insomniacs. At the same time, we also used System Usability Scale (SUS) and semi-structured interviews to evaluate the usability of 8 people with insomnia. In order to understand the usability and willingness of the system. This result shows that the system's usability score is 85, which means a good usability state. The final research results show that our proposed machine learning recommended white noise solution can help patients with insomnia find suitable white noise in a good experience. And this program can help improve their sleep quality.

This research aims to design a white noise recommendation system that can improve insomnia patients and help improve insomnia problems. Finally, this research can reference for related research in the future, such as sound and sleep, human-computer interaction, and machine learning.

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