服務業的成長在經濟發展中顯著而易見，而服務中人員所造成的服務異質性仍是服務提供中維持服務品質的一項難解的難題之一。過去許多研究探討並試圖解決因雙方（服務人員與顧客）的個人因素所產生的品質不穩定，包括了服務人員的工作適配度、員工教育訓練以及工作激勵等角度進行討論；也討論了顧客在服務流程中扮演的角色以及服務預期與服務體驗差距產生的服務認知價值，但卻鮮少討論到服務傳遞者與服務參與者彼此間的互動關係所導致的服務品質差異。
本研究將研究範圍聚焦在觀光導覽服務上，建立一套新的服務流程，將服務精神回歸到以人為本。利用自我組織映射分群法以及協同式過濾推薦法，建立一套全新的機制，將服務人員的選擇及小費金額的給予兩種服務程序，緊扣於顧客認知價值下的選擇。透過自我組織映射法中非監督式的學習網絡，將顧客資料進行分群，來把推薦基準拉近至特質相似的顧客群。再透過協同式過濾推薦法，利用過去顧客的偏好進行新顧客的偏好預測，來推薦合適的服務人員。最後利用自我組織映射分群法，找出被同一服務人員服務過的顧客進行分群，計算出建議的小費金額。在此機制的運行下，能將顧客為主的精神完全融入服務流程，有效提昇顧客的滿意度，同時提昇員工的工作滿意度，造成服務品質提昇的正向循環。
研究結果顯示，研究所提出的機制透過與旅遊決策相關的顧客特性為預測基礎，有效的預測出顧客對不同服務人員的喜好，並指派合適服務人員進行服務。評估指標的表現上，人員推薦系統的平均準確率達81%，最高到85%。研究也證實，在未知服務人員服務特性的狀況下，所推薦出的服務人員，事後觀察其服務特性，發現與顧客所喜好的服務特性雷同，亦即利用此機制選擇服務人員，比起過往隨機指派服務人員的方式，更能滿足顧客對服務的期待。
另外，利用過去顧客的體驗服務經驗與當地的小費文化融合，計算出建議的服務小費金額給予顧客做參考。結果顯示，建議小費金額與顧客對該導覽人員的評分呈現正相關，亦即顧客的平均給分越高者，其小費建議金額會越高。證實了以此系統計算出的小費建議金額，能幫助顧客有所憑據的給予小費金額又不失自身感受到的服務價值。

The growth of service industry impacts the economic development nowadays. However, service heterogeneity is still one of the complex problems to maintain superior service quality. Existing researches attempted to discuss and solve the problem of unstable service quality caused by human beings, such as the appropriateness of job for service providers and education training of staff to standardize the process and control service quality. In addition, some literature investigated the role of customer in service delivering process and the gap between their expectation and perception. Nevertheless, a few researches emphasized on the effect of interaction between service providers and customers that may result in different level of service quality. 
This research proposes a new service process by utilizing self-organizing maps and collaborative filtering to form a hybrid approach (including choosing the service providers and the amounts of given tips) based on customer perception. Through the unsupervised learning network, self-organizing maps can cluster and discover the similar segments of customers. Next, we use collaborative filtering approach to predict new customer’s preference based on similar segments. 
The proposed approach can effectively forecast customer’s preferences among service providers and assign appropriate employee to serve. Based on customer service experience and the local culture for tips, we can calculate the appropriate amount of tips as recommendation for customers. To blend customer-oriented spirit into service process, the proposed method also can effectively improve the level of satisfaction of customers. 
The result shows average value of MAP (mean average value) is 81% and the maximum value of MAP is 85%, which is good for the experiment. The attributes of recommending employees can fit to customer preference more. In other words, our approach can effectively bridge the gap between customer expectation and perception. Finally, the result also reflects on the amount of suggesting tips (i.e., the more the employee can match to the preference, the more tips customer may give).

目錄
目錄	I
圖目錄	II
表目錄	IV
第一章 緒論	1
第一節 研究背景	1
第二節 研究動機	6
第三節 研究問題與目的	10
第二章 文獻探討	12
第一節 自我組織映射圖(Self-Organizing Maps)	12
第二節 協同式過濾推薦法(Collaborative Filtering Recommendation)	15
第三節 服務小費	19
第三章 研究方法	21
第一節 研究架構	21
第二節 自我組織映射網路(Self-Organizing Maps)	22
第三節 協同式過濾推薦(Collaborative Filtering Recommendation)	27
第四節 整合建議小費	32
第四章 研究分析	36
第一節 研究資料	36
第二節 評估指標	40
第三節 研究結果分析	43
第五章 研究結果	54
第一節 結論	54
第二節 管理意涵	59
第三節 研究限制	60
參考文獻	62


圖目錄
圖1.1 世界各國服務業所佔GDP百分比趨勢(前五名)	1
圖1.2 OECD會員國各行業就業人口比率	2
圖1.3觀光業貢獻GDP成長比率	3
圖1.4 SERVQUAL模型	8
圖3.2 SOM示意圖	23
圖3.3 SOM輸出層網路示意圖	24
圖3.4 SOM資料映射示意圖	25
圖3.5 朝向BMU集中示意圖	25
圖3.6使用者基礎(USER-BASED)與項目基礎(ITEM-BASED)之比較	28
圖3.7 箱型圖	34
圖3.8範例箱型圖	34
圖4.1準確率和回應率定義圖	40
圖4.2 資料分群圖（以顧客為基礎）	44
圖4.3 不同導覽人員數的推薦結果比較	47
圖4.4 1000筆資料分群（以不同導覽人員為基礎）	48
圖4.5 各群資料筆數與平均誤差值	49
圖4.6 資料筆數與平均誤差值比較	50
圖4.7 顧客評分與小費建議金額比較	53
圖5.1 評分總分與推薦次數關係圖	55
圖5.2 各評估指標走勢圖	56
圖5.3 目標資料分群離散程度與平均誤差比較	56
圖5.4 導覽特性與推薦次數比較	57
圖5.4 小建議金額與顧客平均分數關係圖	58
圖5.5 服務生命循環圖	59



 
表目錄
表1.1 2012年世界各國服務業所佔GDP百分比(前五名)	2
表1.2全球小費收取概況	5
表1.3 服務中的客製化及服務人員判斷程度	7
表1.4美國小費建議指引範例	9
表2.1 過去研究對自我組織映射法之應用	14
表2.2 近期協同式過濾推薦法研究比較	18
表2.3 近期服務小費研究比較	20
表3.1 使用者評分項目矩陣	29
表3.2 小費建議範例1	32
表3.3 小費建議範例2	33
表4.1 模擬資料整理表	39
表4.2 准確率和回應率元素狀態說明圖	41
表4.2 導覽人員推薦表	44
表4.3 導覽人員評分總數	45
表4.4 研究資料稀疏性	45
表 4.5 導覽人員過去評分分數	46
表4.6 正確率、準確率、回應率與F1指標	46
表4.6 正確率、準確率、回應率與F1指標（續）	47
表4.7 群內資料與平均誤差關係	49
表4.8 A3群組與A4群組內資料	51
表4.9 B1群組內資料	51
表4.10 C2群組與C4群組內資料	51
表4.10 C2群組與C4群組內資料（續）	52
表4.11 導覽人員服務特性受喜好程度	52
表4.12 平均絕對誤差值	52
表5.1 顧客屬性表	54
表5.2 評估指標平均值	55
表5.3 導覽人員特性	57

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