選擇到哪家餐廳用餐的問題，不論旅遊或家居都經常會遇到。大多數的人會先上網，尋找符合自己喜好且評價好的美食。然而網際網路發達，在人人都可上網分享的情況下，造成資訊氾濫超載。使得使用者上網瀏覽資料時，很容易找到不切合需求的資訊。解決此資訊超載的方法之一是餐廳推薦系統。儘管目前有很多的推薦應用程式或是分享平台，諸如TripAdvisor、iPeen愛評網、foursquare…等等，資料豐富但卻沒有針對個人偏好做推薦。 　　本研究有鑒於許多人在品嘗美食之前，會先拍照並在Facebook或Instagram打卡做紀錄、分享給朋友，打卡的次數可能意味著此餐廳的熱門度。且使用者選擇的美食類型偏好也可能受到聚餐目的的影響。因此開發出一款結合社群互動圈以及考量用餐情境的餐廳推薦系統。此系統先利用使用者所選擇的聚餐場合、價位、餐廳類型、熱門商圈等元素篩選出合適的餐廳，再利用Facebook打卡資料取得與使用者偏好相似的好友，依據好友的相似度推算出使用者對餐廳的喜好程度，推薦符合使用者興趣及需求的餐廳，協助使用者能夠更容易地找到自己所喜好的店家。 　　本研究的實作系統，經過評估測試，結果發現結合社群互動圈及考量用餐情境的個人化推薦能讓使用者更容易找到自己所喜好的餐廳，而在推薦內容中顯示好友對餐廳的評論，更有效的幫助使用者作決策。未來本推薦系統所使用之結合情境元素所設計的模式亦可應用至其他領域的推薦平台，如旅遊景點推薦或旅遊住宿推薦。 / Most people face the issue of deciding which restaurant to eat. Searching through the Internet is the first step that people usually do. However the rapid growth of information has overloaded the Internet users, it makes difficult to find the most appropriate information for decision-making. Certainly there are several restaurant recommender systems have been developed to solve the problem, such as TripAdvisor, iPeen, foursquare, etc; but few systems provide personalized and context-based recommendations. 　　The research intends to develop a restaurant recommender system that considers the factors of social network and context. Nowadays, when people eat, they like to take a picture and check in on Facebook or Instagram to share with friends, the numbers of check-in for a restaurant may mean the restaurant’s popularity. In addition, the gathering purpose and personal preferences may also affect the users’ decisions. Therefore the recommender system first used the variables of eating criteria such as place, price, types of food, eating environments to filter restaurants. The system then got the user’s similar friends from check-in data of Facebook. Through calculating friends’ similarity and their preference of restaurants, the recommender system finds the most fitted ones for the user to choose from. 　　The afterward system’s users testing data prove that this personalized and context-based recommendation system provides better information to help the user make their decisions. The same model can be replicated to other domain of recommender platforms.