隨著行動網路的快速發展與web2.0的盛行，人們透過行動設備於任何時間與地點使用網路搜索感興趣的主題、分享生活，網路科技已滲透生活之中，例如：在出遊前，會經由網路得到旅遊資訊來安排行程；遊玩時，則會以行動裝置拍攝照片，並把照片上傳至社群媒體，分享休閒經驗，而這些片段的休閒經驗，亦會成為其他用戶規劃休閒或旅遊行程的參考。但網路上雖有豐富的資訊，卻也伴隨著「資訊過載」的問題，因此，有了個人化旅遊推薦的產生。
個人化推薦以使用者的偏好做推薦，過去相關研究中，個人化旅遊推薦大致有兩個方向─以其它使用者對景點的評分和其它使用者到訪景點的次數做為偏好分析，但前者受限於資料集的資訊，後者受限於到訪次數的真實性，而這兩方法都忽略了旅行者到訪當下的感受並僅單方向給予使用者推薦內容；又由於過去的旅遊行程推薦，多僅單方向給予使用者推薦內容，並未提供可讓使用者自由更換景點的機制，以更加貼近使用者的需求。
因此，本研究將以意見探勘技術分析使用者留下的文字內容中涵蓋的情感，套用多本辭典轉換處理，並以同義字擴增情緒字典，期望經由情感分析所得到的情感分數，能更準確的得知使用者對於該景點的喜好，在僅有文字而沒有評分的資料集中，亦能有效分析其項目偏好。
取得使用者的偏好向量後，考量行走次數、偏好時段類型、偏好停留時間等因素，將推薦的景點組合成行程，綜合以上的因素計算行程分數，推薦Top-q組行程；而為提供使用者更快速且滿意的行程推薦，將加入調整機制，讓使用者可自由更換行程中的單一項目，選擇一定範圍內符合需求的其他偏好景點，以期能在符合使用者需求下，有效率地推薦行程給予使用者。
最後實驗結果證明，本研究提出的方法，在MAE與Hit Rate的評估指標中，衡量預測未知值與行程推薦的表現，皆優於過去的相關研究。

With the rapid development of mobile networks, people share travel experiences through mobile devices. Although tourists can plan itinerary through these fragmented experiences, but there is information overload problem. Therefore, according to the related works in the past, there are two directions to recommend: analyzing the rating scores and calculating the number of visits. However, both methods ignore the tourists’ receptions when they are visiting attractions and can’t support attraction replacement when generating the recommended itinerary. In order to solve above problems, the proposed method will analyze the text emotional preferences furthermore through using sentiment analysis and integrate several factors into recommended itinerary. This study will also allow users to replace locations to fit to their preference.
The experimental results show the performance of the proposed method is better than other methods by MAE and Hit Rate.

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