隨著網際網路普及、雲端平台的建立、行動裝置的應用及社群網站的盛行等，網路上的資訊呈現爆炸性遞增，造成資訊過載的現象，其中社群網站中最受使用者歡迎的Facebook同樣也面臨此問題，隨著粉絲專頁數量增加，讓Facebook使用者不易根據其本身的興趣偏好找到有用且有興趣的粉絲專頁，此外，粉絲專頁經營者也因為無法確實掌握粉絲專頁中為數眾多粉絲成員的需求，而無法有效提高粉絲忠誠度。然而，過往有許多研究提出推薦機制可以解決資訊過載的問題，但多探討推薦機制在線上銷售、電影、音樂、旅遊及電子商務產業上的運用，鮮少有將推薦機制拓展至Facebook粉絲專頁等社群網站上之研究，因此，如何將使用者與粉絲專頁進行媒合正是本研究欲探討的課題。
　　本研究利用皮爾森相似性及預測函數的概念，並以使用者為基礎建構一個針對粉絲專頁的協同過濾推薦機制，並利用K-means集群分析法將Facebook粉絲專頁分群，各集群內分別進行推薦模型之計算，最後，將整體資料及集群資料之推薦結果進行探討及比較。結果顯示，整體資料的部分，透過使用者行為及粉絲專頁之類別屬性預測使用者偏好，2,076筆粉絲專頁之推薦結果為服裝綜合類之粉絲專頁「C H L O E C H E N」優先被進行推薦；集群資料的部分，利用K-means集群分析法產生3個不同特徵的集群，使經營粉絲專頁的企業主了解不同特徵之粉絲專頁應如何經營及洞悉其內部粉絲之需求，進而預測使用者對於未加入的粉絲專頁之可能喜好程度，各集群內優先被進行推薦之粉絲專頁分別為兩性話題類之粉絲專頁「愛情、心語、分享」、服裝綜合類粉絲專頁「C H L O E C H E N」及歌手/樂團類之粉絲專頁「丁噹 Ring」。本研究在學術面提供未來研究者一個新的參考架構；在實務面提供企業在進行Facebook粉絲專頁推薦時的一個參考方向，其推薦模型可提供粉絲專頁經營者進行更有效的行銷策略與經營方針。

　　Along with the spread of the Internet and fast development of cloud platforms, mobile applications and social media, an explosive surge of information on the web has brought about information overload. This phenomenon spread across the Internet, leaving nothing out, including the most popular social media platform, Facebook. As the number of fan pages increases, Facebook users are finding it harder to allocate fan pages that are useful and of their interests, and fan page operators have also encountered problems in their efforts to effectively create fans of high loyalty since it has become harder to meet the needs of the numerous fans on the fan pages. Many studies in the past proposed that a recommendation system can solve the problem of information overload, but most of the studies focused on online marketing, movies, music, travel and e-commerce. However, recommendation system targeting on social media, such as Facebook fan pages, has not been done on a substantial scale. This research aims to explore effective mechanisms for matching users with the fan pages of their interests.
　　This study uses the concepts of Pearson similarity and prediction function with users as the basis of constructing recommendation system targeting on fan pages. K-means cluster analysis was used to group the Facebook fan pages and formulate the recommendation model. Finally, the overall group data and results of the recommendations are analyzed for exploration and comparison. The results of the overall data shows that, by predicting user preferences through the analysis of user behavior and fan page attributes on 2,076 fan pages, the Fan Page of “CHLOECHEN” was selected as the priority recommendation for the general fashion category. To derive the group data, K-means cluster analysis was used to generate three groups of different attributes, which provided information to the fan page owners on how to manage fan pages of different attributes and the needs of the fans of different groups. With the preferences filtered, the fan page owners will be able to predict the degree of interest of non-member users. The priority recommendations for each fan page group are “Love and Sharing” for the relationship category, “CHLOECHEN” for the general fashion category and “Ding Dang ( 丁噹 ) Ring” for the music/band category. This research offers a new reference structure for future studies and a direction for businesses in terms of Facebook fan page recommendations. The Recommendation Model provides fan page operators with an effective strategy for marketing and management.

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