隨著現代資訊科技業的進步加上網路的發達，我們可取得各式各樣的龐大資訊，如何從大量資訊中提供滿足使用者需求的訊息為一重要議題，因此推薦系統這一名詞衍生而出，推薦系統是一種有效代替搜索算法的方式，其最終目標為推薦資訊給使用者，讓使用者能夠找到自己真正想要或需要的資訊。
近年來觀光業的蓬勃發展之下，促使國人對於旅遊方面需求與日俱增，在這情況下，除了旅遊之外如何有效推廣好的住宿是現今熱門的話題，市面上有許多的住宿相關資訊，如Agoda、Booking、Trivago等都各有其特色。除尋找個人喜好的住宿之外，使用者要如何挑選到個人偏好並包含其他有用的資訊為本文研究重點。
本篇論文中，我們提出個人化推薦系統，使用者對系統使用查詢，根據使用者過去的查詢紀錄找出使用者偏好，利用Top-K支配查詢，並用支配數做為標準的直觀評分方式，取前K個數量讓使用者可以不用費心去思考評分的方法作為推薦，除此之外本系統功能包含讓使用者可以查看當季活動、交通資訊及住宿周邊資訊，期望本系統能為使用者帶來更方便且資訊豐富的內容。

With the progress of information technology and the fast development of internet, the amounts of resources and data are increasing rapidly. For the reason, it is essential to find a way to extract useful information from the large amount of data for users. Therefore, recommendation systems are developed for solving this problem. Recommendation system is an effective and efficient way to filter data according to user’s information.
Nowadays, the tourism demand increases every day, tourism industry is very flourishing. There are many web services for recommending hotels, ex. Agoda, Booking, Trivago, etc. In order to provide a better user experience, how to find the hotels satisfying the user need an important issue.
In this thesis, we developed a hotel recommendation system according to user profile. In the developed system we will log user’s query and use it to establish the user profile. By applying the concept of Top-K Dominating Queries, find the hotels according to user’s preference. Besides, our system also provide the surrounding information, includes recent activities, traffic information and weather information, to make users more continent.

第一章 導論 1
1.1 研究動機與目的 1
1.2 論文架構 3
第二章 相關研究背景 4
2.1 天際線查詢 4
2.2 Top-K查詢 6
2.3 Top-K支配查詢 6
2.4 相關應用程式探討 10
第三章 系統架構與推薦系統設計 14
3.1 系統架構 15
3.2 資料庫設計 15
3.2.1資料庫實體關係模型 16
3.2.2資料庫表格 16
3.3系統功能設計 19
3.3.1主頁查詢功能 19
3.3.2住宿總覽功能 22
3.3.3 推薦住宿功能 23
3.3.4交通資訊功能 25
第四章 系統實作 26
4.1開發環境 26
4.2基於TOP-K支配查詢之住宿推薦系統實作 27
4.2.1系統登入畫面 27
4.2.2 主頁查詢 28
4.2.3住宿總覽 29
4.2.4推薦住宿 30
4.2.5交通資訊 31
第五章 結論與未來工作 33
第六章 參考文獻 34

[1]Agoda訂房網. 擷取自 https://www.agoda.com
[2]Booking訂房網. 擷取自 https://www.booking.com
[3]台灣旅宿網. 擷取自 https://taiwanstay.net.tw/
[4]花蓮觀光資訊網. 擷取自 https://tour-hualien.hl.gov.tw/
[5]AsiaYo預定網. 擷取自 https://asiayo.com/zh-tw/
[6]四方通行旅遊網. 擷取自 https://www.easytravel.com.tw
[7]Shuo Shang ; Kai Zheng ; Christian S. Jensen ; Bin Yang ; Panos Kalnis ; Guohe Li ; Ji-Rong Wen. (2014). Discovery of Path Nearby Clusters in Spatial Networks. IEEE Transactions on Knowledge and Data Engineering.
[8]Weiguo Zheng ; Xiang Lian ; Lei Zou ; Liang Hong ; Dongyan Zhao. (2016). Online Subgraph Skyline Analysis over Knowledge Graphs. IEEE Transactions on Knowledge and Data Engineering.
[9]Eleftherios Tiakas ; Apostolos N. Papadopoulos ; Yannis Manolopoulos. (2015). Skyline queries: An introduction. 2015 6th International Conference on Information, Intelligence, Systems and Applications.
[10]Chifumi Nishioka ; Ansgar Scherp. (2016). Profiling vs. time vs. content: What does matter for top-k publication recommendation based on Twitter profiles? IEEE/ACM Joint Conference on Digital Libraries.
[11]Li-rong Xiong ; Ling-yan Wang ; Yu-zhu Huang. (2017). An Approach for Top-k Recommendation Based on Trust Information.IEEE 10th Conference on Service-Oriented Computing and Applications.
[12]Chuan-Ming Liu ; Tien-Chun Wang ; Chuan-Chi Lai ; Li-Chun Wang. (2018). An effective method for top-k dominating query processing over multiple uncertain data streams. 2018 27th Wireless and Optical Communication Conference (WOCC).
[13]Maria Kontaki ; Apostolos N. Papadopoulos ; Yannis Manolopoulos. (2011). Continuous Top-k Dominating Queries. IEEE Transactions on Knowledge and Data Engineering.
[14]Bo Jiang ; Xinjun Du. (2018). Personalized travel route recommendation with skyline query. IEEE 9th International Conference on Dependable Systems, Services and Technologies (DESSERT).
[15]Ken C.K. Lee ; Baihua Zheng ; Cindy Chen ; Chi-Yin Chow. (2012). Efficient Index-Based Approaches for Skyline Queries in Location-Based Applications. IEEE Transactions on Knowledge and Data Engineering .
[16]Mahamudul Hasan ; Shibbir Ahmed ; Md. Ariful Islam Malik ; Shabbir Ahmed. (2016). A comprehensive approach towards user-based collaborative filtering recommender system. International Workshop on Computational Intelligence (IWCI).
[17]Igor N. Gluhih ; Ivan Y. Karyakin ; Lyudmila V. Sizova. (2016). Recommender system providing recommendations for unidentified users of a commerial website. IEEE 10th International Conference on Application of Information and Communication Technologies (AICT).
[18]Sweta Arya ; Debashis Sen ; Balasubramanian Raman. (2016). An automatic personalized photo recommender system based on learning user preferences. IEEE Annual India Conference (INDICON).
[19]Stefano Faralli ; Giorgia Di Tommaso ; Paola Velardi. (2016). Semantic Enabled Recommender System for Micro-Blog Users. IEEE 16th International Conference on Data Mining Workshops (ICDMW).
[20]Qi Yu ; Athman Bouguettaya. (2011). Efficient Service Skyline Computation for Composite Service Selection. IEEE Transactions on Knowledge and Data Engineering.
[21]Liang Chen ; Li Kuang ; Jian Wu. (2012). MapReduce Based Skyline Services Selection for QoS-aware Composition. IEEE 26th International Parallel and Distributed Processing Symposium Workshops & PhD Forum.
[22]FaginRonald. (2001). Optimal aggregation algorithms for middleware. ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems.
[23]Wayne Xin Zhao, Yanwei Guo, Yulan He, Han Jiang, Yuexin Wu, Xiaoming Li. (2014). We know what you want to buy: a demographic-based system for product recommendation on microblogs. The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
[24]Herlocker et al. (2002). An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms. Information Retrieval Volume 5, Issue 4.
[25]Amit Goyal, Laks V. S. Lakshmanan. (2012). RecMax: exploiting recommender systems for fun and profit. The 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD).