在室內空間中，人們會隨機聚集在一起，這類群組稱為暫時性群組。有些室內環境會有播放音樂的需求來吸引用戶，如何在用戶出現前選擇好音樂並提升用戶滿意度便成為室內音樂推薦的主要挑戰。為了解決這項問題，本篇論文提出一個基於奇異值分解的混合型音樂推薦系統，並應用物件的標籤資料以及用戶的打卡資料。首先，在群組推薦中，我們以用戶在室內的歷史與最近打卡記錄來考慮暫時性群組，並作為推測未來群組組成的參考。接著，本系統採用了音樂物件的標籤資料，來選擇更貼近用戶偏好的音樂，並提高推薦結果的多元性。此外。本系統的效能由兩個現實中的資料集連結成的合成資料集來進行實驗。在資料前處理中，我們以地點領域的資料集進行模擬，並將音樂領域中用戶的隱性回饋轉換為評分，來產生資料集中沒提供但實驗中需要的資料。實驗結果展示了本論文提出的系統在不同程度的用戶滿意度標準，以及不同數量的推薦中，效能皆優於根據物件熱門度選擇物件的基線方法，而和基線相比的進步幅度從 13.17% 到 185.00%。

In a room-based environment, people gather randomly and some of them get together for the first time. This kind of groups is called ephemeral groups. Some room-based environments require music to draw customers, and how to select proper music for ephemeral groups in advance of their presence to raise their satisfaction becomes the main challenge of music recommendation in the environments. To solve the problem, we propose a hybrid group recommendation system based on singular value decomposition, tag information of items, and the most important, the check-in data of users. First, in group recommendation, we consider ephemeral groups with users’ historical and current presence in the environment as a reference of the group composition in the near future. Next, to select more music that meets users’ tastes and to increase the diversity of recommendation, we employ tag information of music items. Moreover, the performance of the system is experimented with synthetic data from two real-world datasets. In the pre-processing of data, simulation in location domain dataset and conversion from implicit feedback to ratings in music domain dataset are conducted to obtain the data needed. The result shows that the proposed system outperforms the baseline of popularity on recommending items for users with different levels of satisfaction standards, and the range of improvement the proposed method over the baseline is from 13.17% at least to 185.00%.

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