近年來隨著社群網路(Social network) 的興起，使得 Facebook、Twitter、Google+等等的社群網站快速的興起且廣為接受與喜愛，尤其是 Facebook 具備了娛樂性、社群成熟度、介面豐富、詳細的用戶資訊以及具有應用程式擴充性等等，漸漸改變了使用網路服務的習慣及人際互動的方式。使用者藉由在塗鴨牆(wall)上發表資訊並提供當下的需求作為朋友之間交互的分享與傳播，並提昇友誼之間的互動關係。
本論文建構了一個 Facebook 上即時餐飲商店推薦系統，利用擷取 Facebook 使用者之文字訊息以及文字訊息辨識之相關研究，以機器學習法針對文章的訊息內容及即時蒐集到的餐飲需求，在多維空間中以分群法(clustering)推薦適合的餐飲商店，同時，使用者也可以將該餐飲商店資訊進行分享並作為提昇朋友之間的互動聯繫的議題素材。
本論文在實驗期間針對 57名受測者中蒐集了 1443篇留言，系統在當中辨識出有584 篇是符合餐飲商店需求，也從使用者的回覆訊息以及系統觸發認同、系統推薦認同、系統綜合滿意表的問卷中分析其正確率，每個項目分別至少都有達70%以上的滿意度。此外還針對實驗當中的數據以及開放性問卷來做個案分析，發現使用者對於本系統都覺得餐廳歸類明確，亦能提昇朋友之間的互動情誼，使用者也能夠感到有興趣、實用並且持續使用本系統。

Social network, for example, Facebook, Twitter and Google+, provide novel cannels for enhancing the interpersonal relationships. The most popular one is Facebook, which provides various games, virtual communities, friendly user interface, well scalability for programming and an easy way of obtaining friend’s status.
This paper propose a restaurant recommendation system on the Facebook. A user's messages filter is constructed for feeding back a restaurant recommendation on users’ wall of Facebook. The content of recommendation is composed by the results of clustering the restaurants and users demands on line. The experiments collected 1443 messages which were posted by 57 Facebook users during 7 days. 584 messages were related with the restaurants information inquires, and a recommendation was delivered for each message. The questionnaires illustrated that up to 70% satisfaction after the experiment, and the friendship between users friends on Facebook were improved by analyzing the content and frequency of interactions.

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