近年來，隨著網際網路的快速發展，資訊量已經由不足變成超載。而目前推薦系統已應用在各式各樣的領域來解決資訊超載的問題，但美食領域的應用卻還很少，也沒有考慮使用者當下的情境。另外，隨著資訊時代的來臨，美食論壇蓬勃發展，論壇中的文章也漸漸變成影響使用者選擇的因素之一。因此，本研究實作了一個美食推薦系統，系統的情境資訊包含了實體環境中的天氣、溫度，以及虛擬網路環境中的熱門程度。推薦系統的建立，使用了Microsoft的機器學習平台Azure Machine Learning Studio所提供的混合式推薦演算法，而我們也自行撰寫了R與Python的程式碼進行情境變數的探勘與計算，並將之融入推薦系統，最後排序出符合使用者偏好及當下情境的推薦結果。

In recent years, with the rapid development of the Internet, the amount of information has changed from insufficient to overload. At present, the recommendation system has been applied in various fields to solve the problem of information overload, but there are few applications in the food field, and the context of the user is not considered. In addition, with the advent of the information age, food forums have flourished, and articles in the forum have gradually become one of the factors that affect users' choices. Therefore, this research has implemented a food recommendation system. The contextual information of the system includes the weather and temperature in the physical environment, as well as the popularity in the virtual network environment. The establishment of the recommendation system uses the hybrid recommendation algorithm provided by Microsoft's machine learning platform Azure Machine Learning Studio, and we also wrote the code of R and Python for the exploration and calculation of context variables, and integrated it into the recommendation. The system finally sorts out the recommendation results that meet the user's preference and the current situation.

論文審定書 i
致謝 ii
摘要 iii
Abstract iv
目錄 v
圖目錄 vii
表目錄 viii
第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 3
第三節 研究流程 4
第二章 文獻探討 6
第一節 推薦系統 6
第二節 基於內容 6
第三節 協同過濾 8
第四節 情境感知 10
第三章 系統架構與研究步驟 16
第一節 系統架構之平台選擇 16
第二節 研究系統架構 22
第四章 方法與結果 27
第一節 推薦模型建立 27
第二節 情境變數 29
第三節 熱門度變數 30
第四節 不同推薦方法的效果比較 35
第五章 結論與建議 40
第一節 研究結論 40
第二節 研究限制與未來方向 40
參考文獻 42

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