旅程推薦系統是推薦系統中一個重要的應用。它可以跟據人們的喜好推薦使用者喜歡的旅程。然而現有的推薦系統並沒有辦法同時考量多天旅程、旅程距離、使用者需求及使用者喜好來做推薦，僅能處理其中一至兩項。此外，現有推薦系統需要對景點資料庫內所有景點都進行處理，所以他們會需要較多的資料存取次數及計算時間。此篇論文藉由分析使用者過往的打卡紀錄，使用創新的時間區間的概念並搭配多天旅程推薦演算法來找出使用者有興趣且符合使用者需求的旅程。同時，我們將R-tree應用至旅程推薦系統中，並因此降低資料存取的次數及計算時間。最後，我們提出一個評估旅程演算法的依據，它可以比較各個演算法之優劣。實驗將驗證我們提出方法的有效性。

A trip recommender system can generate suggested itineraries for users based on their preferences. However, current systems are not capable of simultaneously considering trip length, distance, user requirements and preferences when making recommendations, being only equipped to consider one or two of these variables at one time. Also, to generate recommendations the system must process all attractions in the database, requiring more data access and longer processing time.
We analyze the check-in records of users and utilized a new concept of time intervals combined with a multiple days trip algorithm to produce itineraries compatible with the interests and needs of users. By applying R-tree to the trip recommender system, we reduce data access times and computation time. Lastly, we propose a trip evaluator equation that can be used to compare the strengths and weaknesses of each algorithm. Experimental results verified the effectiveness of our method.

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