臺灣博碩士論文加值系統

旅遊業係有無煙囪工業之稱，不需要設廠即可創造獲利。旅客們在景點停留的時間越長，花費也越多。他們通常從網路上找尋想要的旅遊資訊，在這資訊爆炸的時代，推薦系統可以幫助人們不用花很多的時間就能取得有用的資訊。現有的旅遊推薦系統偏向提供單一項目的推薦，像是餐廳或是景點，或是做簡單的屬性配對便推薦給旅客，而且，不同的旅客喜歡的旅遊路線也不一樣。
因此，本研究提出一個推薦方法，利用OWA權重的概念在旅遊偏好上，讓使用者更容易表示其偏好和屬性的優先順序。除此之外，人格特質和旅遊偏好之間的關係也是本研究的目的之一，針對不同的人格特質給予不同的推薦結果。接著將推薦方法實作在Andoird App上，並利用使用者滿意度來評估本研究提出的推薦方法。結果顯示，大部分的人都有多重人格特質，沒有經驗開放性的人最不喜歡遺跡旅遊，沒有親和性、盡責性和神經質的人喜歡低價位的旅遊行程，沒有經驗開放性的人不喜歡天數多的旅遊。除此之外，推薦的結果顯示OWA在本研究是最好的推薦方法。本研究結果可以提供給旅遊利益相關人，讓旅遊業者可以開發不同的團體套裝旅遊路線，或提供給想去旅遊的人做參考。

No-chimney industry is another name of tourism industry. Without factories, output value could also be created. The longer the tourists stay at attractions, the more expense they spend. Usually, tourists searched tourism information on the internet. In the age of information explosion, a recommender system could help people retrieve useful information without spending a lot of time. The exist recommender systems in tourism only provided results whether single items or merely in accordance with conditions set by tourists. Additionally, different people prefer different routes.
Therefore, this study proposed a recommended method which applied the concept of OWA weighting on travel preferences. It makes tourists express their preferences and priorities more easily. Besides, this study also investigate the relationship between tourists’ personality traits and travel preferences, and provide different result to different personality traits. Then, these recommended methods were developed to an android app recommender system is built. Last but not least, user satisfaction is utilized to evaluate the performance. The results show that: (1) Most of people have multiple personality trait, (2) Most of the participants like Leisure tourism, (2) People without Openness who dislike Heritage tourism, (3) Participants without Agreeableness, Conscientiousness and Neuroticism who prefer lower price, (4) People without Openness who dislike long-day trip. Furthermore, the results of recommendation show that OWA weights is the best recommended method in this study, and OWA weight is intermediate weight relative to the listing weight methods. At last the results could provide tourism stakeholder to identify customers’ personality trait and recommend travel routes.

摘要 i
Abstract ii
Contents iii
List of Tables v
List of Figures vi
1.Introduction 1
1.1 Background 1
1.2 Motivation 3
1.3 Purpose 3
1.4 Organization of Thesis 4
2. Related Works 5
2.1 Recommender Systems 5
2.1.1 Types of Recommender Systems 5
2.1.2 Services Offered by Recommender Systems in Tourism 6
2.2 Travel Preference of Group Packaged Tour 7
2.2.1 Group Package Tour 7
2.2.2 Attributes of Group Package Tour 7
2.2.3 Review of Travel Preference 8
2.3 Personality Trait 9
2.3.1 Definition of Personality Trait 9
2.3.2 Classification of Personality Trait. 9
2.3.3 Big Five Personality Trait 10
2.4 Weighting Techniques 12
2.4.1 OWA Weight 12
2.4.2 Rank Order Centroid Weight 14
3. Methodology 15
3.1 Research Process 15
3.2 Questionnaire Design 17
3.2.1 Personality Trait 17
3.2.2 Travel Preference 19
3.2.3 Priority of Travel Preference 19
3.3 Collection of Routes 22
3.3.1 Selection of Travel agencies 22
3.3.2 Collection of Routes and Pre-Process 24
3.4 Recommended Method 27
3.5 System Analysis and Development 28
3.5.1 System Development Environment 28
3.5.2 System Architecture and Process 28
3.5.3 Introduction of Recommender System Interface 29
3.6 System Evaluation 33
4. Analysis and Results 34
4.1 Data Analysis 34
4.2 Evaluation of Recommended Method 42
4.3 Findings 43
4.3.1 Travel Preference 43
4.3.2 Personality Traits 43
4.3.3 Priority of Attributes 43
4.3.4 Recommended method 44
4.3.5 Follow up 45
5. Conclusion 46
Reference 47
Appendix A Questionnaire 53

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