由於適地性服務的活絡，人們隨時可以藉由手機，依照自己所在地點與需求，查詢符合自己有關的商家或路線，亦可藉由社交網站的打卡，標註許多地點以及相關評論，隨著這類涵蓋地點與關鍵字的空間資訊愈來愈多，藉由這些資訊中的地點與關鍵字，近年來發展出許多spatial keyword query的相關研究，其中根據使用者分為兩種研究方向，一為顧客面向，二為地圖物件面向；在顧客方面，主要藉由顧客下達關鍵字查詢，系統根據顧客地點與下達的關鍵字，找出最符合關鍵字與距離最近的地圖物件；而地圖物件方面，則是藉由收集前面所述顧客的查詢結果，與該地圖物件所在位置與現有關鍵字，輸入系統並回傳能帶給該物件最多顧客的關鍵字，然而目前現有的研究中有關地圖物件面向的查詢，皆以人潮帶來商機為出發點，找出能帶給地圖物件最多顧客的關鍵字，卻忽略了實際上帶給地圖物件利益的重要因素─價格，當加入價格因素後，問題不再只是如何吸引最多人，而是必須衡量關鍵字的各種售價情形，亦即薄利多銷、厚利少銷的權衡，故本論文將「價格」因素加入spatial keyword query中，以地圖物件為立場，找出能帶給該物件最大營收的關鍵字與對應售價組合，並提出了3種optimal solution演算法與1種approximate solution演算法處理該查詢，最後藉由一系列的實驗，來評估4種演算法的效率。

Due to the availability of the location based service, people can use the mobile phone to check the relevant stores or routes according to their location and needs. They can also mark many places and related comments by social media, such as FaceBook. In recent years, with the increasing spatial information of such places and keywords, many researches on spatial keyword query have been developed through the locations and keywords in these messages. According to the user, it is divided into two research directions, one for the customer and the other for the map object. On the customer side, the customer mainly issues a keyword query, and the system finds the map object that most closely matches the keyword and the distance according to the customer location and the released keyword. In the case of map objects, by collecting the results of the customers described above, user enters the system with the location of the map object and the existing keywords, then system returning the keywords that can bring the most customers to the object. However, the current research on map object queries is based on the business opportunities brought by the crowds to find the keywords that can bring the most customers to the map objects, but ignores the important factors that actually bring the benefits of the map objects, that is “price”. When the price factor is added, the problem is no longer just how to attract the most people, but must measure the various selling prices of the keyword. Therefore, this paper adds the "price" factor to the spatial keyword query, and uses the map object as a standpoint to find out the keyword and corresponding price combination that can bring the maximum revenue of the object, and proposes three optimal solution algorithms and one approximate solution algorithm to processes the query, and finally evaluates the efficiency of the four algorithms through a series of experiments.

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